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2023

Oncology Nurses, Compassion Fatigue and General Health: A Oncology Nurses, Compassion Fatigue and General Health: A

Mixed-Methods Study Mixed-Methods Study

Michelle Rampersad Walden University

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Walden University

College of Nursing

This is to certify that the doctoral dissertation by

Michelle Rampersad

has been found to be complete and satisfactory in all respects, and that any and all revisions required by the review committee have been made.

Review Committee Dr. Carolyn Sipes, Committee Chairperson, Nursing Faculty Dr. Deborah Lewis, Committee Member, Nursing Faculty Dr. Bonnie Fuller, University Reviewer, Nursing Faculty

Chief Academic Officer and Provost Sue Subocz, Ph.D.

Walden University 2023

Abstract

Oncology Nurses, Compassion Fatigue and General Health:

A Mixed-Methods Study

by

Michelle Rampersad

Post-Masters Nurse Practitioner, University of South Florida, 2007

MSN, Drexel University, 2005

BSN, University of Phoenix, 2003

Dissertation Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Philosophy

Nursing

Walden University

March 2023

Abstract

Current literature on compassion fatigue is expansive, but there is a lack on the

relationship between compassion fatigue and general health complaints in oncology

nurses; how they perceive compassion fatigue and its relation to their general health.

Using Pender’s health promotion model, this mixed methods study addressed how

oncology nurses perceive compassion fatigue and whether a correlation exists between

compassion fatigue and general health complaints. Data were collected from a sample of

55 oncology nurses through two separate Survey Monkey links. All 55 participants

completed quantitative data points including a demographic questionnaire, the

Professional Quality of Life 5 tool, and the Giessen Subjective Complaints brief form.

Participants selecting the second link also completed qualitative questionnaires (n = 15).

Pearson’s correlation test revealed statistically significant positive correlations: burnout

with exhaustion and musculoskeletal complaints (p = .000 and .036, respectively) and

secondary traumatic stress with exhaustion, gastrointestinal complaints, and

cardiovascular complaints (p = .000, .022, and .007, respectively). Qualitative data

revealed nine themes including fatigue and being overwhelming. Combining quantitative

and qualitative data showed the strength of the relationship between compassion fatigue

and general health complaints. Oncology nurses recognize compassion fatigue as a very

real phenomenon and feel that it needs to be addressed. Social implications of this new

research, showing that compassion fatigue is a problem affecting nurses that needs to be

addressed could lead to improved retention of nurses in the field.

Oncology Nurses, Compassion Fatigue and General Health:

A Mixed-Methods Study

by

Michelle Rampersad

Post-Masters Nurse Practitioner, University of South Florida, 2007

MSN, Drexel University, 2005

BSN, University of Phoenix, 2003

Dissertation Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Philosophy

Nursing

Walden University

March 2023

Dedication

This dissertation is dedicated to the many people in my life who have helped me

get to this point.

First to my mom, in heaven, for starting me down my nursing career and being

my guardian angel every step of the way.

To my dad, who made sure I never lost sight of the end of this very long tunnel.

His love and support kept me going through it.

To my husband Keith, who told me from the beginning, many years ago, that I

could achieve anything I set my mind to. His faith in me has always been a light that has

guided me through. He has always supported me in any endeavor I have undertaken.

To my son Dillon, who pushed me to keep going when I wanted to give up.

To my many other family members, friends, co-workers, and colleagues who

were my cheerleaders along the way; they pushed, prodded, laughed, and cried right there

with me.

Without all of you this would not have been possible.

Acknowledgements

There are many people I would like to acknowledge for their support along this

journey.

Dr. Carolyn Sipes who would not let me give up when I wanted to. She would call

me and motivate me countless times to keep at it. Without her this would not have ever

been completed.

Dr. Deborah Lewis and Dr. Bonnie Fuller for their valuable input and guidance

along this journey.

The many people at Walden University for their assistance in many different

aspects of the entire PhD program.

Thank you to all of you for your commitment to higher education.

i

Table of Contents

List of Tables ..................................................................................................................... iv

List of Figures ......................................................................................................................v

Chapter 1: Introduction to the Study ....................................................................................1

Background ....................................................................................................................1

Problem Statement .........................................................................................................3

Purpose of the Study ......................................................................................................4

Research Questions and Hypotheses .............................................................................4

Theoretical Framework ..................................................................................................5

Conceptual Framework ..................................................................................................7

Nature of the Study ........................................................................................................8

Definitions....................................................................................................................12

Assumptions .................................................................................................................13

Scope and Delimitations ..............................................................................................13

Limitations ...................................................................................................................14

Significance..................................................................................................................15

Summary ......................................................................................................................16

Chapter 2: Literature Review .............................................................................................18

Literature Search Strategy............................................................................................19

Theoretical Foundation ................................................................................................19

Conceptual Framework ................................................................................................22

Concepts and Definitions .......................................................................................22

ii

Socioecological Framework .................................................................................. 23

Connection Between Theoretical and Conceptual Framework ...................................23

Literature Review.........................................................................................................24

Compassion Fatigue in Other Disciplines............................................................. 25

Compassion Fatigue in Oncology Nursing ........................................................... 26

General Health Complaints Associated With Compassion Fatigue ..................... 27

Researched Interventions for Compassion Fatigue............................................... 31

Summary ......................................................................................................................46

Chapter 3: Research Method ..............................................................................................48

Research Design and Rationale ...................................................................................48

Setting ..........................................................................................................................50

Role of the Researcher .................................................................................................50

Methodology ................................................................................................................51

Population ............................................................................................................. 51

Sampling Procedures ............................................................................................ 51

Procedures for Recruitment, Participation, and Data Collection .......................... 52

Instrumentation ..................................................................................................... 52

Data Analysis Plan ................................................................................................ 55

Threats to Validity .......................................................................................................57

Issues of Trustworthiness .............................................................................................58

Ethical Procedures .......................................................................................................59

Summary ......................................................................................................................59

iii

Chapter 4: Results ..............................................................................................................61

Setting .........................................................................................................................61

Demographics ..............................................................................................................62

Data Collection ............................................................................................................65

Data Analysis ...............................................................................................................65

Results ..........................................................................................................................67

Qualitative Results ................................................................................................ 68

Quantitative Results .............................................................................................. 70

Evidence of Trustworthiness........................................................................................76

Threats to Validity .......................................................................................................77

Summary ......................................................................................................................78

Chapter 5: Discussion, Conclusions and Recommendations .............................................80

Interpretation of Findings ............................................................................................81

Limitations ...................................................................................................................83

Recommendations ........................................................................................................83

Implications..................................................................................................................84

Conclusion ...................................................................................................................85

References ..........................................................................................................................86

Appendix A: Data Collection Tools ..................................................................................98

Appendix B: Socioecological Framework .......................................................................102

iv

List of Tables

Table 1. Demographic Data (N = 51)................................................................................ 63

Table 2. Demographic Data From Qualitative Subset (n = 15) ........................................ 64

Table 3. Qualitative Themes ............................................................................................. 67

Table 4. Do You Believe You Suffer From Compassion Fatigue and Why Do You Think

That? ......................................................................................................................... 70

Table 5. Pearson Correlation Exhaustion, Burnout, and STS ........................................... 72

Table 6. Pearson Correlation Gastrointestinal Complaints, Burnout, and STS ................ 72

Table 7. Pearson Correlation Musculoskeletal Complaints, Burnout, and STS ............... 73

Table 8. Pearson Correlation Cardiovascular Complaints, Burnout, and STS ................. 74

v

List of Figures

Figure 1. Qualitative Themes ............................................................................................ 67

Figure 2. Joint Display of Mixed Results ......................................................................... 75

1

Chapter 1: Introduction to the Study

The topic of this study was compassion fatigue and the general health of oncology

nurses. This study was important and needed to be carried out because, according to

research (Kohli & Padmakumari, 2020; Reiser & Gonzalez, 2020), oncology nurses may

be at a higher risk for developing compassion fatigue because of the nature of their

profession. The implications for positive social change involve improving the levels of

compassion fatigue and the general health of oncology nurses.

In this chapter, I present a brief background, the problem and purpose statements,

and the research questions and hypotheses. The chapter also includes a discussion of the

theoretical and conceptual frameworks used to address the study concepts as well as the

nature of the study, definitions, assumptions, scope and delimitations, limitations,

significance, and a summary of the chapter.

Background

Oncology nurses are an essential component of the cancer care team, caring for

cancer patients along their treatment trajectory. As such, they are exposed to the

prolonged suffering of the patient and family. This suffering can include but is not

limited to cancer-related symptoms, treatment-related side effects, fear, uncontrolled

pain, and death and dying. This repeated exposure can lead to compassion fatigue and

increased general health complaints.

Compassion fatigue has been classified as a diminished ability to care for others

as a direct result of repeated exposure to patients’ continual suffering (Cavanaugh et al.,

2020; Cross, 2019; Stamm, 2010). Investigation of compassion fatigue among oncology

2

nurses needs to be undertaken because research has shown that when nurses suffer from

compassion fatigue, there is an increase in nurse health complaints, patient complaints,

and medical errors, as well as a decrease in nursing performance (Cross, 2019; Harris &

Griffin, 2015; Sorenson et al., 2017). Compassion fatigue has also been shown to lead to

increased nursing turnover (Lee et al., 2018) and intent to leave the field (Wells-English

et al., 2019).

Compassion fatigue can lead to physical and psychological consequences (Harris

& Griffin, 2015; Lombardo & Eyre, 2011; Xie et al., 2019). Physical effects include

headaches, nausea, vomiting, diarrhea, and insomnia, while the psychological effects may

be depression, anxiety, irritability, and self-doubt (Cross, 2019; Sorenson et al., 2017).

The 2020 State of the World Nursing Report stated that the nursing shortage is expected

to be at a standstill of almost 6 million nurses by 2030, indicating that the incoming and

outgoing nurses balance out (Challinor et al., 2020). Addressing the professional quality

of life of oncology nurses may increase the retention of nurses which is a current problem

(Lee et al., 2018; Wells-English et al., 2019).

Addressing compassion fatigue in nursing through research will help with the

retention of nurses by decreasing levels of compassion fatigue that research shows lead to

turnover (Lee et al., 2018). Lee et al. (2018) evaluated nursing turnover related to

compassion fatigue at one Southern California Magnet hospital and found that in 2015

the turnover rate related to compassion fatigue was 17.2% for their facility alone.

According to the NSI National Health care Retention and RN Staffing Report (NSI

Nursing Solutions, 2021), the hospital turnover rate for staff registered nurses (RN’s) was

3

18.7%, with the average cost of turnover per RN being $40,038. Wells-English et al.

(2019) evaluated the levels of compassion fatigue and nurses’ intent to leave the nursing

field, discovering that higher levels of compassion fatigue indicated an increased intent of

nurses to leave the field. They recommended that additional studies be conducted to

evaluate interventions to combat compassion fatigue and decrease the turnover rate of

nursing staff. Arimon-Pages et al. (2019) explored the professional quality of life and

anxiety in oncology nurses and found that over half of the nurses in the study had

moderate to high levels of compassion fatigue and moderate to high levels of anxiety.

Yilmaz and Uston (2019) investigated sociodemographic and professional factors that

affect nurses’ professional quality of life. They found that the longer the time spent with

the patient, the greater the risk of compassion fatigue. They also reported that improving

professional conditions (e.g., shorter shifts, fewer shifts, receiving department-specific

education, and supporting nurses) increases the nurses’ professional quality of life.

Based on the previously discussed studies, research clearly shows that oncology

nurses are at an increased risk of developing compassion fatigue. I will discuss the

previous research in more depth in the next chapter. The gap in the literature is evaluated

for a correlation between compassion fatigue and general health complaints in oncology

nurses while adding the qualitative data to explore the nurses’ perceptions of compassion

fatigue.

Problem Statement

Oncology nurses may be at a higher risk of compassion fatigue than other nursing

specialties due to the very nature of the patient population they care for (Kohli &

4

Padmakumari, 2020; Reiser & Gonzalez, 2020). Oncology nurses care for patients

suffering from prolonged illness from cancer, cancer-related treatments, cancer-related

pain, and often death (Jakel et al., 2016; Pehilvan & Guner, 2020). Compassion fatigue

needs to be addressed through research to prevent the consequences that arise from it.

The gap in the literature that I evaluated was exploring a correlation between

compassion fatigue and general health complaints in oncology nurses while adding

qualitative data to explore the nurses’ perceptions of compassion fatigue.

Purpose of the Study

The purpose of this mixed method convergent concurrent study was twofold. The

quantitative purpose was to examine the relationship between compassion fatigue and

health complaints. The qualitative purpose was to explore nurses’ perceptions of

compassion fatigue. I chose the mixed-methods approach because it provides quantitative

data that can show statistical significance while at the same time adding the richness and

depth of qualitative data that explores the oncology nurses’ lived experiences.

The use of mixed-methods research will help to provide valuable information on

the experiences of oncology nurses as it relates to compassion fatigue and general health

complaints. The results of this study will also provide information on whether there is a

correlation between compassion fatigue and general health complaints in oncology nurses

as measured by the Professional Quality of Life 5 (ProQOL 5) tool and the Giessen

Subjective Complaints List-Brief Form (GBB-8).

Research Questions and Hypotheses

The following research questions and hypotheses guided this study:

5

Research Question 1 (RQ1; qualitative): What are the perceptions of oncology

nurses regarding compassion fatigue?

Research Question 2 (RQ2; quantitative): What is the correlation between

compassion fatigue and general health complaints in oncology nurses as measured by the

ProQOL 5 and the GBB-8?

H02: There is no correlation between compassion fatigue and general health

complaints.

H12: There is a correlation between compassion fatigue and general health

complaints.

The variables studied are nurses’ compassion fatigue and general health

complaints.

Theoretical Framework

Oncology nurses are often described as being caring and compassionate; however,

research has shown that caring for patients along the cancer continuum has its

consequences (Harris & Griffin, 2015; Kohli & Padmakumari, 2020; Lombardo & Eyre,

2011; Reiser & Gonzalez, 2020; Xie et al., 2019). Oncology nurses are at a higher risk of

developing compassion fatigue than other nursing disciplines (Kohli & Padmakumari,

2020; Reiser & Gonzalez, 2020). Compassion fatigue can have adverse effects on a

person’s physical and psychological health and their professional quality of life (Kohli &

Padmakumari, 2020; Reiser & Gonzalez, 2020; Xie et al., 2019).

I chose Pender’s health promotion model as the theoretical framework for this

study because increasing awareness of compassion fatigue and the risk to general health

6

will increase the use of relaxation techniques to promote healthy behavioral changes. I

also chose Pender’s health promotion model because compassion fatigue is a health

problem that has adverse health effects, including headaches, gastrointestinal problems,

depression, anxiety, and fatigue (see Harris & Griffin, 2015). Addressing compassion

fatigue may positively affect nurses’ mental and physical health.

Pender’s (2011) health promotion model was first developed in 1982 and then

revised in 1996 and 2002 due to changing perspectives and findings. The model

evaluated factors influencing health behaviors, including eight health beliefs. I used some

of these eight beliefs to support oncology nurses’ awareness of the problem of

compassion fatigue by providing information about compassion fatigue after completing

the questionnaires and surveys.

The ProQOL5 (see Appendix A) is a 30-item questionnaire developed by Figley

in the 1980s to measure the quality of life in healthcare professionals (Stamm, 2010).

This tool measures both compassion fatigue and compassion satisfaction. I used this tool

to determine the participants’ levels of compassion fatigue. The questionnaire contains 20

questions related to burnout and secondary traumatic stress that are used to calculate the

score for compassion fatigue and 10 questions that measure compassion satisfaction.

The GBB-8 (see Appendix A) is a validated, eight-item questionnaire tool to

evaluate general health complaints and was adapted from the 24-item subjective

complaints list (Kliem et al., 2017). I chose this tool because researchers have shown that

nurses suffering from compassion fatigue have physical and psychological health

7

complaints (see Cross, 2019; Harris & Griffin, 2015; Lombardo & Eyre, 2011; Sorenson

et al., 2017; Xie et al, 2019).

Conceptual Framework

The conceptual framework for this study is Plano Clark and Ivankova’s (2016)

socioecological framework. The socioecological framework (see Appendix B) contains

research questions, the type of data collected, and the inferences and five overlapping

circles that explain the mixed-methods research approach. The three outer rings that

address the mixed-method research contexts are personal contexts, interpersonal contexts,

and societal contexts. Personal contexts include experience with compassion fatigue,

knowledge in self-care, expansive oncology experience, and pragmatism. Interpersonal

contexts include being up to date on good clinical practice standards.

Social contexts include that this study was conducted in the United States in the

oncology field and that I had university support. The social change addressed with this

topic is compassion fatigue and how it correlates with general health in oncology nurses.

By increasing awareness, administrators can use these data to implement different

interventions to help their nurses.

The logical connection between the framework presented and the nature of the

study includes assessing whether there is a correlation between compassion fatigue levels

and general health complaints and what perceptions oncology nurses have regarding

compassion fatigue. Grant and Osanloo (2014) pointed out that the theoretical foundation

reflects personal importance to the researcher regarding the topic of the study.

Compassion fatigue is very personal to me because I have seen oncology nurses deal with

8

it and have seen the health issues arising from it both personally and professionally.

Pender’s health promotion model was helpful in my quest to address compassion fatigue

among oncology nurses and improve their overall health and well-being. Plano Clark and

Ivankova’s (2016) socioecological framework guided the research and supported the

study as well as ensured that all requirements of the study were met.

Nature of the Study

To address the research questions in this mixed methods study, I used a

convergent, concurrent, mixed methods design (see Gray et al., 2017). This design is

proper when a researcher wants to confirm findings within a single study using a single

sample. In this design, quantitative and qualitative data are collected simultaneously,

analyzed separately, and then integrated to interpret and draw conclusions (Gray et al.,

2017). The rationale for using this design was to gain a deeper understanding of

compassion fatigue and the general health of oncology nurses. Few studies have used a

mixed methods approach to evaluate these variables and none have looked at compassion

fatigue and general health. Studies that did use a mixed-methods approach all used a

different design: Giarelli et al. (2016) used a descriptive design, Zajac et al. (2017) used a

sequential design, and Pfaff et al. (2017) used an embedded experimental design. The

mixed method used in the current study comes from Creswell et al. (2011, as cited in

Plano Clark & Ivankova, 2016). It focuses on the participants’ real-life experiences

utilizing multiple methods for data collection and combining the results of these multiple

methods. The convergent, concurrent, mixed-methods design uses questionnaires and a

survey with eight open-ended questions with written responses. The variables are

9

compassion fatigue and general health complaints. I analyzed the data using both

descriptive and inferential statistics and thematic coding, I analyzed them separately at

first and then merged the results to provide a deeper understanding of the data.

The design supported the collection of quantitative data using a demographic

questionnaire, the ProQOL 5 tool, and the GBB-8 and qualitative data using a

questionnaire consisting of eight open-ended questions with written responses. The

qualitative questionnaire was coded following Saldana’s (2021) coding process with first-

and second-level coding to derive themes. I chose manual in vivo coding as the first-level

coding method and manual thematic coding as the second-level coding method. I

measured quantitative data from the ProQOL 5 and the GBB-8 with statistical analysis

through IBM SPSS Statistics (Version 27; see Wagner, 2016). The data were then

merged to explore underlying themes that correlated with compassion fatigue levels in

the ProQOL 5 data and health complaints on the GBB-8. The qualitative data provided

data on what oncology nurses perceive about compassion fatigue; there were also other

significant data gleaned from the qualitative data.

The ProQOL 5 tool was originally developed by Dr. Figley back in the late 1980s

and has since gone under revision and refinement (Stamm, 2010). The scale measures

compassion fatigue via burnout and secondary traumatic stress (STS) and then

compassion satisfaction. The compassion fatigue scale is distinct. The tool was designed

for continuous use, meaning in its entirety. Data were collected on all three parts of the

scale as the best way to support its validity and reliability. Measurement of the ProQOL 5

has 30 questions on a Likert scale and the directions for scoring are in the manual that

10

accompanies it. The reliability is 0.88. Burnout scores less than 23 are reflective of

positive feelings in the workplace; scores greater than 41 equal a higher risk of burnout.

STS scores greater than 43 indicate a high level of STS and the need for intervention. The

two scales, burnout and secondary traumatic stress equal the compassion fatigue scale.

There is no statistical difference across gender, age, race, income, or years in the current

position or field (Stamm, 2010). This tool has proven both validity and reliability with

over 200 published articles and more than 100,000 articles on the internet.

The GBB-8 (see Appendix A) was adapted from the Giessen Subjective

Complaints List (GBB-24), a German measure of subjective health complaints (Kliem et

al., 2017). The GBB-8 has eight items rated on a Likert scale ranging from 0 (not at all)

to 4 (very much), indicating how troubling each complaint is perceived. This adaption

was developed and validated in a large population study with over 2000 participants. The

psychometric analyses included confirmation of factor structure, classical item analysis,

and measurement invariance tests. The sample was deemed to serve as a normal group

for the population. To determine construct validity, correlations with measures of anxiety,

depression, alexithymia, and primary care contact were computed. Analyses revealed a

Cronbach’s alpha of 0.88, the comparative fit index was 0.980. This applies to the four-

factor model that is represented in the GBB-8 (i.e., exhaustion, gastrointestinal

complaints, musculoskeletal complaints, and cardiovascular complaints). Construct

validity of the scale is evidenced by the correlation coefficients of the GBB-8 total score

with depression and anxiety were r = .56. The GBB-8 score also showed high

correlations (r = .44, p < .001) with the number of primary care provider contacts in the

11

previous year, as well as the number of physician consultations (r = .45, p < .001; Kliem

et al., 2017).

The basic demographic questionnaire (see Appendix A) included items such as

age, gender identification, years of nursing experience and years of oncology nursing

experience, and inpatient or outpatient status. For the qualitative component, a written

survey was completed with eight open-ended questions about compassion fatigue and

general health oncology nurses.

Data points included the eight questions from the qualitative questionnaire, the

nine questions on the demographic tool, the 30 questions on the ProQOL 5 tool, and the

eight questions on the GBB-8. The ProQOL 5 (see Appendix A) collects data on

compassion fatigue and compassion satisfaction related to a person’s employment. The

GBB-8 (see Appendix A) collects data on health complaints in four major subcategories,

exhaustion, gastrointestinal complaints, musculoskeletal complaints, and cardiovascular

complaints. The qualitative questionnaire (see Appendix A) explored the nurse’s lived

experiences of compassion fatigue and their general health. The data were evaluated to

assess what oncology nurses understand about compassion fatigue and if there is a

correlation between compassion fatigue levels and general health complaints. Using the

qualitative questionnaire, I looked for codes, themes, and subthemes to validate findings

of the effect on compassion fatigue and the general health of oncology nurses. Combining

the qualitative and quantitative data added the evidence needed to answer the research

questions proposed.

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Definitions

Compassion fatigue as a concept has many different definitions; however, the

broadest definition comes from the ProQOL manual (Stamm, 2010) as the negative

aspect of the work of caring for others. Cavanaugh et al. (2020), in a systematic review

and meta-analysis of compassion fatigue, recognized that it impacts the general health

and effectiveness of professionals in healthcare and eventually affects patient care. Cross

(2018) conducted a concept analysis that identified compassion fatigue as a complex

concept with consequences that affected professionals, organizations, and clients/patients.

Compassion satisfaction is defined here as a concept but it is not a variable under

study. Compassion satisfaction is defined as the satisfaction a person gets from helping

others (Stamm, 2010).

General health is defined as “a state of complete physical, mental, and social

well-being and not merely the absence of disease or infirmity “(World Health

Organization, n.d., p. 1)

Professional quality of life is “the quality one feels about their work as a helper”

(Stamm, 2010, p. 8). The term helper includes any profession in the position to help

others in times of crisis. There are both positive and negative facets of one’s profession

that affects one’s professional quality of life. Positive professional quality of life has been

termed compassion satisfaction, whereas the negative has been termed compassion

fatigue (Stamm, 2010).

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Assumptions

Assumptions that can be seen or heard when dealing with oncology nursing are

that it must be a depressing and sad field to work in. Another assumption is that all cancer

patients die or suffer. A third assumption could be that oncology nurses all have effective

coping strategies. These assumptions are essential to address because they can lead to

misconceptions about oncology nurses, how they feel about the profession, and how they

cope with their day-to-day job. Research shows that oncology nursing has unique

features, as previously discussed, that puts them at a higher risk to suffer from

compassion fatigue (Yu et al., 2016). There is a gap in evaluating for a correlation

between compassion fatigue and general health complaints in oncology nurses with the

added qualitative data exploring the nurses’ perceptions of compassion fatigue. Obtaining

a baseline of data about what oncology nurses perceive about compassion fatigue and

demonstrating that there is a correlation to general health, as this study was designed to

do, will assist with planning of future interventional studies to prevent, and combat

compassion fatigue.

Scope and Delimitations

The specific aspects of the research problem chosen for this study are compassion

fatigue and general health complaints in oncology nurses. The reason that these were

chosen is that compassion fatigue has been shown to affect general health in oncology

nurses (Harris & Griffin, 2015; Lombardo & Eyre, 2011; Sorenson et al., 2017; Xie et al.,

2019). These have been studied both quantitatively and qualitatively, but there have not

been many mixed methods studies to look at them simultaneously from both angles. This

14

study explored what oncology nurses perceive about compassion fatigue and whether

there was a correlation between compassion fatigue levels and general health complaints.

Research shows that this is important for oncology nurses to practice self-care

techniques to improve their levels of compassion fatigue and general health (Kohli &

Padmakumari, 2020). The boundaries of the study were that only active oncology nurses

who have been working in the field over a year were included. Another boundary is that

this study was being conducted via SurveyMonkey (https://www.surveymonkey.com/),

which may limit people from wanting to participate.

There was one main theory that was evaluated but determined not to be relevant

with regards to this study. The theory of the nurse as wounded healer (Conti-O’Hare,

2002 as cited in Christie & Jones, 2013) was not chosen since it relates to personal

trauma not secondary trauma as seen in patient care.

Limitations

One limitation of this study could have been sample size. The sample may be too

small or too large since it is a survey design using SurveyMonkey; however, that was not

a problem. A second limitation was that there may be incomplete data; this was handled

by aiming for a number over that indicated by the G*Power analysis to allow for the four

incomplete surveys that were returned. A third limitation could have been the enrollment

of participants due to inclusion criteria, but this was not a problem. These possible

limitations will be addressed in detail in Chapter 3.

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Significance

This study was significant in that data would reveal whether there is a correlation

between compassion fatigue levels and general health complaints and what oncology

nurses perceive about compassion fatigue and their general health. Oncology nurses were

chosen because previous research has shown that oncology nurses may be at a higher risk

for developing compassion fatigue (see Giarelli et al., 2016; Gomez-Uriquiza et al., 2016;

Kohli & Padmakumari, 2020; Resier & Gonzalez, 2020; Wentzel et al., 2019; Wu et al.,

2016; Xie et al., 2020). Compassion fatigue is often seen in health service professions

due to the nature of their work (Gomez-Urquiza et al., 2016). The prolonged exposure to

people who are in pain, suffering, and/or dying takes its toll on a professional’s quality of

life which equates into compassion fatigue (Harris & Griffin, 2015; Kohli &

Padmakumari, 2020; Stamm, 2010; Wells-English et al., 2019). Compassion fatigue is

the loss of the ability to care for others (Lombardo & Eyre, 2011; Stamm, 2010). By

addressing compassion fatigue in oncology nurses, social change may be affected by

increasing professional quality of life, improving general health, and decreasing the

number of nurses leaving the field. Lee et al. (2019) found that it is estimated to cost the

healthcare organization $37,700 to $58,400 dollars to turnover one nurse. Wells-English

et al. (2019) found that increased levels of compassion fatigue correlated with increased

intent to leave the field. Interventions that evaluated ways of combatting compassion

fatigue included providing a provider resilience mobile application, knitting, biannual

survivor events and an accelerated recovery treatment program (see Anderson &

Gustavson, 2016; Fleming et al., 2020; Jakel et al., 2016; Lee et al., 2019). To date, little

16

is known as to whether one intervention is more effective than others in combating

compassion. One of the eight steps for effecting social change in the video Social Impact

of a Dissertation that Dr. Iris Yob pointed out was with practice (Laureate Education,

2015g). Addressing compassion fatigue in oncology nursing has the protentional to

improve nurses’ satisfaction with their profession. This research could support social

change on a larger scale if it supports that oncology nurses believe compassion fatigue is

a very real problem that affects their health and they believe it needs to be addressed.

Summary

In summary, this chapter has provided a general overview of the research study. It

has covered a brief background, the problem and purpose statement, and research

questions and hypotheses. It also covered the theoretical and conceptual frameworks used

to address the study concepts, the nature of the study, definitions, assumptions, scope and

delimitations, limitations, and the significance of the study.

This research can effect positive social change by improving the professional

quality of life of oncology nurses and their general health. The next two chapters will

include an in-depth analysis of the current status of the literature for this study and the

variables under study. Chapter 2 will cover a review of the literature including the

literature review search strategy, current status of the research variables, and an in-depth

review of the theoretical and conceptual framework used in this study. Chapter 3 will

cover methodology and include an in-depth examination of the research design,

instrumentation, study procedures, and data analysis plan. Chapter 4 will cover data

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collection, analysis, and quantitative, qualitative, and mixed results. Chapter 5 will cover

interpretation of the findings, limitations, recommendations, and implications of the data.

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Chapter 2: Literature Review

Compassion fatigue has been studied in multiple different professions such as law

enforcement, firefighters, lawyers, social workers, and educators (Cuartero & Campos-

Vidal, 2019; Essary, 2020; Grant et al., 2019; Kim et al., 2020; Tilby & Holbrook, 2019).

Oncology nurses have been identified as being at a higher risk for developing compassion

fatigue due to the nature of the patient population that they care for (Kohli &

Padmakumari, 2020; Reiser & Gonzalez, 2020). Compassion fatigue is associated with

many different physical and psychological complaints, work-related and patient safety

concerns, and a financial toll (Harris & Griffin, 2015; Lee et al., 208; Lombardo & Eyre,

2011; Wells-English et al., 2019; Xie et al., 2019)). Research has been undertaken that

looks at compassion fatigue levels in oncology nursing quantitatively and qualitatively;

however, to date there is no mixed methods study that looks simultaneously at

compassion fatigue levels and general health complaints in oncology nurses while at the

same time exploring how oncology nurses perceive compassion fatigue and their general

health. Therefore, I conducted a mixed-methods research study to explore the perceptions

of oncology nurses regarding compassion fatigue and to determine whether there is a

correlation between compassion fatigue levels and general health complaints. I chose the

mixed-methods approach because it provides quantitative data that can show statistical

significance while at the same time adding the richness and depth of qualitative data that

explores the oncology nurses’ lived experiences.

This chapter covers the literature search strategy utilized for this study as well as

Pender’s (2011) health promotion model, which is the theoretical foundation, and Plano

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Clark and Ivankova’s (2016) socioecological framework, which was used as the

conceptual framework. There will also be a literature review related to key variables and

concepts. The chapter finishes with a summary of the current state of the literature and a

conclusion.

Literature Search Strategy

The literature review search strategy included a search of EBSCO, PubMed and

Google Scholar for articles looking for the following keywords and various combinations

of them: compassion fatigue, nursing, oncology nurses, oncology nursing, general health

complaints in oncology nurses, and interventions for compassion fatigue. The years

included were from 2015 to current and included the seminal work for the Professional

Quality of Life Tool (Stamm, 2010) and Nola Pender’s health promotion model (2011).

Some older articles were also included due the nature of their content and evidence.

Theoretical Foundation

The theoretical foundation of this study is Pender’s health promotion model,

which was first developed in 1982 and revised in 1995 and again in 2002 due to changing

perspectives (Pender, 2011). The model was designed to help nurses understand patient

behaviors to promote healthy lifestyle changes. Pender’s health promotion model is based

on expectancy value theory and social cognitive theory (Pender, 2011). The expectancy

value theory explains that people will participate in measures, to achieve goals that are

possible to achieve and that provide value. Social cognitive theory suggests that thoughts,

behaviors, and the environment all interact and that for people to alter behavior they have

to alter their thinking and environment. The philosophical roots of the health promotion

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model are based on the reciprocal interactive worldview, where all people are viewed as a

whole but parts can be studied separately.

The health promotion model has seven assumptions that reflect both behavioral

science and nursing perspectives. These assumptions include that people will seek to

change conditions that will have a positive impact on health but also create an acceptable

balance between change and stability. Another assumption is that people have the

capability to reflect on their own self-awareness and realize the need for behavioral

changes. A fourth assumption is that as people interact with their environment, they

transform the environment and themselves over time. The next assumption is that health

care professionals are part of a person’s interpersonal environment and produce changes

on a person throughout their lifespan.

The two assumptions that are most important to this study include that a person

actively seeks to regulate behavior and that self-initiated alterations of one’s environment

are essential to promote behavioral change. I consider these the most important because

the oncology nurses are electing to participate in a study that could increase awareness of

a problem and may affect behavioral change thus affecting their health.

Pender’s health promotion model has 14 theoretical propositions that provide a

basis for research on health behaviors; I will discuss the ones applicable to this study. The

first is that people commit to engage in behaviors from which they anticipate gaining

personal valued benefits (Pender, 2011). In this study, once the nurses become aware of

the problem of compassion fatigue, they may begin to do their own research on it to help

themselves. The third and fourth propositions suggest that if there is a higher feeling of

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self-efficacy, and positive change results from the behavior, there are fewer perceived

barriers and increased commitment to action, respectively. Once nurses are aware of

compassion fatigue and its effect on their health, they may begin to practice techniques to

combat it and if they feel better, they are more like to continue them.

The following proposition, from the health promotion model, states that people

are more likely to enact the behavior when significant others model, expect, and support

the behavior. Additionally, the external environment can influence participation in health-

promoting behaviors. As previously stated, if the nurses are feeling better and others

notice they will support the nurses in continuing the techniques they are using, possibly

modifying their behavior or the environment to help. The next proposition is that the

greater the dedication to the behavior change, the more likely it will be maintained over

time. For instance, if the nurses who partake in the study feel that this increased

awareness has helped them and they notice a change for the better; they are more likely to

continue using the techniques they found helpful. However, if there is a competing

demand or a more attractive alternative, the dedication to the change in behavior is less

likely to occur.

The last proposition, from the health promotion model, that is useful for this study

is that people have the ability to modify multiple different aspects to create inducements

for promoting healthy behavioral change. If the nurses are wanting to participate and

wanting to learn how to help themselves, they can change different aspects of their day to

help improve levels of compassion fatigue.

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Pender’s health promotion model was chosen because compassion fatigue is a

health problem, and the model promotes healthy behavioral changes. The negative health

effects of compassion fatigue can include headaches, gastrointestinal problems,

depression, anxiety, and fatigue (Cross, 2019; Harris & Griffin, 2015; Powell, 2020;

Sorenson et al., 2017). By addressing compassion fatigue, nurses’ mental and physical

health can be improved along with levels of compassion fatigue. Pender’s health

promotion model also related to the research questions, which focused on increasing

awareness of compassion fatigue through evaluating compassion fatigue and general

health complaint levels.

Conceptual Framework

Concepts and Definitions

The concepts that are being explored are compassion fatigue and general health

complaints. Compassion fatigue as a concept has many different definitions; however, the

broadest definition comes from the ProQOL manual (Stamm, 2010) as the negative

aspect of the work of caring for others. It impacts the general health and effectiveness of

professionals in healthcare and eventually affects patient care (Cavanaugh et al., 2020). It

is a complex concept with consequences that affect professionals, organizations, and

clients/patients (Cross, 2018). General health is “a state of complete physical, mental, and

social well-being and not merely the absence of disease or infirmity” (World Health

Organization, 1948, p. 1). Professional quality of life is “the quality one feels concerning

their work as a helper” (Stamm, 2010, p. 8). The term helper includes any profession in

the position to help other people in times of crisis. There are both positive and negative

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facets of a person’s profession that affect professional quality of life. Positive

professional quality of life has been termed compassion satisfaction, whereas negative

has been termed compassion fatigue (Stamm, 2010).

Socioecological Framework

The conceptual framework that I used was Plano Clark and Ivankova’s (2016)

socioecological framework (see Appendix B for a visual graphic). The framework

addresses this mixed methods research through personal contexts, interpersonal contexts,

and societal contexts. Personal contexts include experience with compassion fatigue,

knowledge in self-care, expansive oncology experience, and pragmatism. Interpersonal

contexts include being up to date on good clinical practice standards. Social contexts

include that this study was conducted in the United States, in the oncology nursing field,

and that I had university support.

Connection Between Theoretical and Conceptual Framework

I chose Pender’s health promotion model because compassion fatigue is a health

problem. The model has eight beliefs that can be assessed and used to target promoting

awareness of a problem and changing behaviors to achieve health (Pender, 2011).

Pender’s health promotion model can help oncology nurses deal with compassion fatigue

and improve their overall health and well-being. Plano Clark and Ivankova’s (2016)

socioecological framework provided the framework to guide the research and support

achievement of a complete study.

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Literature Review

“Compassion fatigue” was first coined by Joinson in 1992 as she witnessed her

nurses losing their ability to care about their patients; at this time, it was introduced as a

synonym for burnout (Joinson, 1992 as cited by, Harris & Griffin, 2015). However,

burnout differs from compassion fatigue in that it arises more from the chronic stressors

of the work environment rather than caring for traumatized patients (Cavanaugh et al.,

2020). Later, psychologist Dr. Figley began to identify compassion fatigue as a secondary

traumatic stress disorder due to being more descriptive of the cost of caring for

traumatized individuals (Figley, 1993, as cited in Sorenson et al., 2017). Figley defined

compassion fatigue as the cost of caring for individuals suffering from traumatic events

(Ruiz-Fernandez et al., 2020). Figley also developed the ProQOL tool to measure

compassion fatigue in professionals (Stamm, 2010). The defining attributes of

compassion fatigue include sudden onset, emotional and physical exhaustion, apathy,

helplessness, desensitization, and depersonalization (Henson, 2020). In contrast,

burnout’s defining attributes were gradual onset, emotional exhaustion, cynicism and

hopelessness. Compassion fatigue arises from caring for traumatized patients and may

affect patients care more severely due to the nurses decreased ability to care about them

not necessarily for them (Cavanaugh et al., 2020). Compassion satisfaction is the pleasure

one gets from caring for others (Ruiz-Fernandez et al., 2020; Stamm, 2010). The balance

between compassion fatigue and compassion satisfaction is what can be used to

determine professional quality of life (Ruiz-Fernandez et al., 2020).

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Compassion Fatigue in Other Disciplines

Compassion fatigue has also been studied in professions other than just health

care providers. Firefighters often work in hazardous conditions where they are exposed

repeatedly to a victim’s trauma, loss of property, or loss of life. Kim et al. (2020) found

that the greater the risk in the working environment, the greater the risk for compassion

fatigue in firefighters. Those who work in law enforcement are also exposed to

continually hazardous conditions and traumatic events; however, studies show that they

do not suffer from high levels of compassion fatigue (Grant et al., 2019; Turgoose et al.,

2017). Additionally, in a quantitative research study of 270 social workers, over 90%

reported medium to high levels of compassion fatigue due to day-to-day involvement

with people in physical, mental, and emotional distress and listening to the stories that

they tell (Cuartero & Campos-Vidal, 2019). Though there is not much data available on

compassion fatigue in lawyers and judges; one article highlighted the fact that secondary

traumatic stress does occur in this population due to listening and replaying traumatic

events in the courtroom thus increasing the risk of compassion fatigue in this population

(Tilby & Holbrook, 2019). Research also shows that educators suffer from high levels of

compassion fatigue due to interacting and supporting children who are victims of violent

crime (Essary, 2020; Perez-Chacon et al., 2021). Working with special needs children,

gifted children, or behaviorally challenged students increases the risk of compassion

fatigue among educators (Perez-Chacon et al., 2021).

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Compassion Fatigue in Oncology Nursing

Oncology nurses care for cancer patients across the cancer continuum; these

patients may be suffering from cancer-related symptoms, treatment-related side effects,

fear, and the grieving process. In oncology nursing, the nurse cares not only for the

patient but also for the caregivers. Research has shown that 60% of oncology nurses

suffer from moderate to high levels of compassion fatigue (Ortega-Campos et al., 2020).

In a study of 2,509 oncology nurses, there was a 62.79% prevalence rate of burnout and a

66.84% prevalence rate of secondary traumatic stress, the two components of compassion

fatigue (Algamdi, 2022).

Risk factors other than caring for patient that have been found to contribute to

compassion fatigue include the nurse’s grief or loss experiences (Ko & Kaiser-Larson,

2016) as well as age of the nurse, number of shifts worked, amount of time per week

worked, and if they received department specific education (Yilmaz & Uston, 2019;

Zajac et al., 2017). Heavy workload, increased expectations, lack of resources, ineffective

management, passive coping strategies, and a long-term mutual relationship with the

patients has also led to increased levels of compassion fatigue (Harris & Griffin, 2015;

Kelly, 2020; Yu et al., 2016). Personality traits like neuroticism have also been associated

with compassion fatigue (Yu et al., 2016). Neuroticism is the trait disposition to

experience adverse effects (Widiger & Ottmann, 2017). Work environments with poor

levels of supervisory and coworker support, decreased decision-making ability, and

increased psychological demands can also lead to increased risk of compassion fatigue

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(Malliet & Read, 2021). Combating compassion fatigue and supporting oncology nurses

may hopefully keep more nurses in the field and draw new ones to the field.

General Health Complaints Associated With Compassion Fatigue

Compassion fatigue has been shown to affect physical health,

psychological/emotional health, and create behavioral/work-related problems. Physical

symptoms that can be seen with compassion fatigue are many. Common signs and

symptoms include headaches, chronic exhaustion (emotional and physical), weight loss,

and insomnia (GoodTherapy, 2020). Other symptoms include lack of energy and appetite

changes (Zajac et al., 2017). Wentzel et al. conducted a qualitative study to attempt to

define compassion fatigue from oncology nurse’s standpoint; one of the symptoms that

came out of that study was emotional fatigue that nurses defined as “fatigue from within”

(2019, p.4). Xie et al. (2020) conducted a systematic review that included 21 articles and

involved 6,533 oncology nurses; physical symptoms that were found to be associated

with compassion fatigue included exhaustion, headaches, sleep disorders, constipation,

diarrhea, and gastrointestinal upset.

Upton (2022) identified physical symptoms including those above but also

identified increased blood pressure, weight gain, stiff neck, and immune dysfunction.

There was also an increase in cardiovascular diseases and diabetes related to compassion

fatigue according to an older study by Aycock and Boyle (2009). In a concept analysis of

compassion fatigue by Sorenson et al. (2017), all these symptoms were also found

including cardiovascular changes. Kohil and Padmakumari (2020) also discuss the

physical symptoms of headaches, insomnia, and reduced appetite.

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Psychological or emotional symptoms also abound with compassion fatigue. In

the systematic review by Xie et al. (2020), these symptoms included irritability,

depression, and substance abuse. In a qualitative study Wentzel et al. (2019) identified

psychological symptoms of emotional loss and emotional exhaustion. In a systematic

review by Gomez-Urquiza et al. (2016) that included 27 articles and 11,107 oncology

nurses, they explored levels of burnout for oncology nurses specifically looking for the

three components of burnout; emotional exhaustion, depersonalization, and personal

accomplishment. They did find that oncology nurses did suffer from high levels of

emotional exhaustion. Since burnout is closely related to compassion fatigue and has the

same psychological symptoms of emotional exhaustion and depersonalization it was felt

appropriate to be included here.

Zajac et al. (2017) identified psychological symptoms of compassion fatigue as

being apathy, callousness, and indifference. Upton (2022) identified psychological

symptoms of compassion fatigue, including those mentioned above, but also, cynicism,

anxiety, discouragement, and detachment. Other psychological problems found with

compassion fatigue include a feeling of emptiness, a decreased sense of purpose or ability

to feel joy, a diminished sense of personal accomplishment, anger, and blaming

(Sorenson et al., 2017). An article by Powell (2020) also pointed out psychological

symptoms of anger, irritability, heightened anxiety, and irrational fears. She also

discussed the increased risk of alcohol and drug usage. Kohli and Padmakumari (2022)

discussed psychological symptoms of stress related pathology and depressive symptoms.

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Work-related problems that have been found with compassion fatigue can cause

an increased risk to the patient and nursing safety. These include inability to focus or

concentrate, calling in or increased absenteeism, chronic lateness, or overworking

(Sorenson et al., 2017). Powell (2020) describes work related issues related to

compassion fatigue as the dread of working with patients, absenteeism, and impaired

ability to make decisions and care for patients. Kohli and Padmakumari (2020) also

identified impaired decision making and medical errors as consequences of compassion

fatigue. Upton (2022) identifies work related problems of compassion fatigue as

decreased productivity, poor performance, poor professional judgment, and increased

medical errors. She also reports an increase in patient dissatisfaction.

Kelly (2020) reported that when nurses are suffering from compassion fatigue,

they are less likely to be engaged with their patients and more likely to make errors.

Harris and Griffin (2015) included work-related problems of an increase in poor

judgment and patient dissatisfaction. It is essential to discuss patient satisfaction as it

directly is related to hospital reimbursement which in turn affects the resources available

to the nursing staff, including having enough staff. In another study, Wells-English et al.

(2019) discussed that compassion fatigue led to an increase in situations where more

errors could occur and a decrease in productivity. Their study explored compassion

fatigue and how levels of compassion fatigue led to increased turnover of nursing staff.

They found that the higher the level of compassion fatigue the higher the chance the

nurse would leave the nursing field. This turnover leads to newer, less experienced nurses

entering the field which can lead to increase in errors and increased risk to patient safety.

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When we start discussing the work-related problems seen with compassion fatigue this in

turns leads into the financial impact of compassion fatigue on the organization involved.

While that is not the topic of this research, the outcomes of this research do have the

ability to impact organizational outcomes.

The social problem identified and the focus of this research is that oncology

nurses may be at higher risk of compassion fatigue than other nursing specialties due to

the very nature of the patient population they care for (Kohli & Padmakumari, 2019;

Reiser & Gonzalez, 2020). Caring for cancer patients exposes the nurses to prolonged

illness from cancer and cancer related treatments, cancer related pain, and death (Jakel et

al., 2016). Compassion fatigue needs to be addressed through research to prevent the

consequences that arise from it; physical and psychological problems, risk to patient

safety, and nursing turnover.

Research has shown that compassion fatigue can lead to both physical and

psychological consequences. Physical effects include headaches, nausea, vomiting,

diarrhea, and insomnia just to name a few, this was discussed above in detail.

Psychological effects may be depression, anxiety, irritability, and self-doubt, also

discussed above. Work related factors include poor performance and increase in medical

errors. The 2020 State of the World Nursing report expects that the nursing shortage to be

at a standstill of almost 6 million nurses by 2030 (Challinor et al., 2020). Addressing

compassion fatigue in nursing through research and finding effective interventions will

help with retention of nurses. Lee et al. (2018) looked at nursing turnover as it related to

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compassion fatigue at one Southern California Magnet hospital and found that in 2015

the turnover rate was 17.2% related to compassion fatigue for that facility alone.

Wells-English et al. (2019) conducted a study looking at levels of compassion

fatigue and nurses’ intent to leave. Their results showed that higher levels of compassion

fatigue indicated an increase intent of nurses to leave the field. Recommendations from

their study were for additional studies to look at interventions to combat compassion

fatigue thus potentially leading to a decrease in the turnover of nursing. According to the

NSI National Health Care Retention and RN Staffing Report (NSI Nursing Solutions,

2021), the hospital turnover rate for staff RN’s is at 18.7% with the average cost of

turnover per RN $40,038. By researching compassion fatigue, general health complaints

and techniques oncology nurse use; this researcher hopes to increase awareness of

compassion fatigue in nurses and improve general health complaints thus leading to

improvement in compassion fatigue and the general health of the oncology nursing

workforce.

Researched Interventions for Compassion Fatigue

Many studies have been done to look for interventions to combat compassion

fatigue. In this next section, I will review these interventions which include interventions

such as educational programs, resiliency training, retreats, camps, crafting and

mindfulness, just to name a few. This section of literature review is included because it

supports the need for research on compassion fatigue.

Two articles examined the availability of interventions within the employment

facilities. Aycock and Boyle (2009) surveyed oncology nurses across the United States

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looking for accessibility to onsite professional resources, educational programs,

and/retreats to address compassion fatigue; there were 103 responses. Their results

revealed that up to 60% had some sort of on-site professional resource (employee

assistance programs, support groups, etc.), up to 30% had access to educational

opportunities and only 10% had access to off-site retreats. Wentzel and Brysiewicz

(2017) conducted an integrative review of the literature looking at facility-based

interventions to combat compassion fatigue; 31 studies met eligibility requirements. The

aim of their study was to assess the effectiveness of infacility interventions, their

feasibility, and the nurses’ experiences with them. Out of the 31 studies, four did not

conduct an evaluation of the intervention, 11 showed that burnout, compassion fatigue,

and secondary traumatic stress scores decreased. In comparison, three studies reported no

changes in compassion fatigue or burnout scores. Four authors measured health

complaints and found an improvement after the intervention. Three other studies reported

on death anxiety and end of life stress and that these levels decreased with the

intervention. Two studies revealed increased team camaraderie and self-reflection with

their intervention. Finally, one reported that the intervention resulted in a reduction of

staff turnover. Regarding the feasibility of an infacility intervention, Wentzel and

Brysiewicz (2017) found many variations of time, scheduling, and types of interventions

and how it was incorporated into the facility that overall feasibility could not be

determined.

In the literature search, three articles were chosen that looked at mindfulness as

the intervention. Owens et al. (2020) used a 3-minute mindfulness intervention that is the

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shortest that has ever been tested. They used a quasi-experimental design with a single

group. The intervention was a 3-minute mindful breathing session. Their sample size

started at 45 with the final ending at 32 participants. The research hypothesis was to

explore if the intervention would decrease levels of compassion fatigue over 4 weeks.

The nurses were instructed to do the 3-minute breathing sessions, 3 times a day for 4

weeks. They did find that there was a significant reduction in burnout (p = .0113) and

STS (p = .0053) on the ProQOL tool; the two components of compassion fatigue.

Limitations to this study were that they only used critical care nurses and had a relatively

small sample size though enough to achieve statistical power.

Duarte and Pinto-Gouveia (2016) conducted an abbreviated mindfulness-based

intervention using oncology nurses. They conducted a nonrandomized study with an

experimental arm and control arm. Initially 94 nurses agreed to participate however only

48 completed initial pre- and post-intervention data due to poor follow-up, not high

dropout. There were 29 in the experimental arm and 19 in the control arm. The

intervention was a 6-week-long group intervention consisting of didactic and experiential

exercises, there was one session a week lasting 2 hours. The authors used seven different

tools pre-intervention, post-intervention, and at 3 months post intervention. Only six

participants did the 3-month set so this data was not analyzed. Their results did reveal a

significant reduction in compassion fatigue as measured by the ProQOL 5 tool, but it was

not statistically significant. Limitations in the study were sample size and poor follow up.

This researcher also believes that the number of tools that were used led to lack of

participation on follow up; there were seven tools with 121 total questions to answer.

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The third study was conducted by Delaney (2018). This study was a mixed

observational research pilot study to evaluate the usefulness of an 8-week mindful self-

compassion training program. There were 13 participants in the study. The intervention

was a generic 8-week-long training that taught the nurses how to respond with positivity

in difficult moments instead of negativity. There was a 2.5-hour weekly session for the 8

weeks along with a half-day retreat. The focus was on self-compassion and mindfulness.

Pre and post data was collected on multiple tools, however, for the basis of this literature

review we will continue only to discuss the results of the ProQOL 5 tool as compassion

fatigue was the topic of this study. Delaney’s results did show a statistically significant

reduction in burnout (p = .03) and in STS (p = .05); the two components of compassion

fatigue. There was also a large effect size as measured by Pearson correlations; burnout (r

= -.60) and STS (r = -.54) correlated to mindfulness. The qualitative data collected by

Delaney that emerged after the training was all positive and supported the use of the

intervention. Limitations are that it was a pilot study and as such had a small sample size

and no control arm. These three studies all support the use of mindfulness as a possible

intervention for combating compassion fatigue.

The following study that will be reviewed involves self-compassion and its

effectiveness on compassion fatigue. Delaney (2018) also used self-compassion

education but in combination with mindfulness. In Galiana and colleagues (2022) study

they conducted a cross-sectional survey of 296 palliative care professionals. The survey

contained 6 tools including the ProQOL 5 tool and the self-compassion scale. They found

a small to moderate effect size using Pearson’s correlation of the three types of self-

35

compassion measured: self-kindness (r = -.296), mindfulness (r = -.309), and common

humanity (r = -.164). The p values for all three of these were < .010 which does indicate

statistical significance. This study does show that increasing self-compassion in nurses

can help combat compassion fatigue which does concur with the Delaney (2018) study

findings.

Shingler-Nace et al. (2018) discussed that moral distress, compassion fatigue,

post-traumatic stress and burnout were all complications of caring for others. They

conducted a quality initiative project that looked at understanding the risk and prevalence

at their facility. They followed the PDSA (Plan, Do, Study, Act) approach to quality

improvement projects. The interventions they put into place included workshops for

nurses that involved an overview of compassion fatigue, self-help techniques, awareness,

and mindfulness. After this workshop was completed, they then instituted compassion

rounds that occurred on a specific day and time on the unit; these focused on providing

employee support and needs. If the nurse was in distress during these compassion rounds,

then a timeout was instituted that allowed the nurse to leave the floor for 10 to 15 minutes

while the coordinator running the rounds monitored her patients. Their data revealed no

statistical difference between the pre and post data with compassion rounds. What was

interesting in their data was that even nurses who were satisfied with their working

environment still were at risk for compassion fatigue.

Yilmazer et al. (2020) researched the effects of dance and movement therapy on

compassion fatigue as measured by the ProQOL 5. This was a semi experimental pilot

study with proposed three arms. Forty-two participants were invited to participate

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however only eight completed the training. Since the sample size was so limited that

there was only the intervention arm. The intervention was conducted once a week for 8

weeks with 60 minutes for each session and involved different dance and movement

therapies based upon multiple models. These sessions were supervised via Skype by a

certified psych movement therapist. The results did show a decrease in compassion

fatigue levels pre to post intervention with a mean score of 28 down to 15.75, indicating

that there is a benefit of dance and movement on compassion fatigue.

Anderson and Gustavson (2016) conducted a study that looked at knitting and its

effects on compassion fatigue. Oncology nurses from a comprehensive cancer center

were invited to participate. Thirty-nine nurses completed the study. Oncology nurses

were taught to knit by Project Knitwell, a nonprofit group. Knitting supplies were left in a

respite lounge located on the unit. The authors did not report compassion fatigue scores

even though this was the purpose of their study; they did however report a significant

change in burnout level; mean went from 24.72 to 22.91, pre to post intervention. This

study has several limitations the most important being not having a consistent trainer

available to the nurses.

Copeland (2021) conducted a quasi-experimental pilot study looking at brief

workplace interventions and their effect on burnout, compassion fatigue, and teamwork.

Her study did incorporate multiple different interventions available to the participants.

These different interventions were all 5 minutes long and included meditation, journaling,

gratitude, outside, and control. Participants (n = 23) were randomized to one of the five

groups (meditation n = 4, journaling n = 4, gratitude n = 5, outside n = 5, and control n =

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2; three dropped out). The intervention period was 6 weeks long. Data were reported for

20 participants who completed pre and post testing. All feedback was positive except for

one comment about journaling, as it added to the nurse’s stress when she was busy.

Looking at the pre and post mean for burnout and STS, the components of compassion

fatigue, we do see a decrease across all the scores in all the groups except for burnout and

this one increased slightly. Journaling and gratitude showed the largest effect size. This

study supports using multiple interventions that are easy to use and easily accessible to

the staff to combat compassion fatigue.

The concept of resiliency has been studied in multiple ways to assess its impact

on compassion fatigue. The following is a review of six studies that have used various

forms of resiliency training to affect change in compassion fatigue scores.

Klein et al. (2018) conducted an interventional study using a commercially

prepared resiliency program. A convenience sample of 18 was chosen, but only eight

completed the entire 6-month study; 12 completed pre and post intervention data. Data

was collected pre-intervention, post-intervention, and at 6 months. Educational sessions

were conducted in three 90-minute sessions during work hours. Data revealed that pre

and post intervention there was no statistical difference in burnout and STS; mean scores

went from 27.3 to 26.75 (p = .49) and 26.1 to 26.3 (p = .91), respectively. At 6 months

burnout was 25.6 and STS was 26.4. This study had many limitations to it and the authors

stressed that those limitations needed to be considered when interpreting the data.

The second study that looked at the use of a resiliency program was conducted by

Pehlivan and Guner (2020). They conducted a randomized control trial with 125

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oncology nurses randomized to one of three arms: experimental I (n = 34) experimental II

(n = 49) and control (n = 42). The intervention was a compassion fatigue resiliency

program conducted as 5 hours per day for 2 days (experimental I) or 2 hours per week for

5 weeks (experimental II). Data analysis was conducted at preintervention,

postintervention, 3 months, 6 months and 1 year. The results of the data indicate that the

only statistically significant results seen were with the experimental II arm at post-

intervention versus control with a p-value of .020. Further data analysis indicated that

compassion fatigue scores worsened overtime. This study does not support resiliency

programs for compassion fatigue.

In an older study by Potter et al. (2013), that was not the case. Potter and fellow

researchers conducted a descriptive pilot study using a compassion fatigue resiliency

program. They conducted the program over 5 weeks with 90-minute sessions. There was

a total of 13 participants. Data was collected preintervention, postintervention, at 3

months and at 6 months. Pre-intervention nurses were at high risk for compassion fatigue

based on burnout and STS scores; 23.46 and 19.76 respectively. Both components did

decrease after the intervention, however, only the score was statistically significant at 6

months with a p-value of .044. This study did support the use of a resiliency program for

compassion fatigue.

In a cross sectional pre and post intervention study carried out by Kestler and

colleagues (2020), they also looked at a resiliency program for use against compassion

fatigue. The program was taught once a week for 3 weeks and each session was an hour

long. Data were collected using the Secondary Traumatic Stress Scale, which does

39

measure and reports compassion fatigue. Data was collected pre-intervention, post-

intervention and at 3 months. Twenty-five nurses completed the full study and the results

showed that almost all nurses had a decrease in levels of compassion fatigue that were

statistically significant at a p-value < .001. This study does show statistical significance

for the usefulness of a resiliency program in combating compassion fatigue.

Jakel et al. (2016) conducted a quasi-experimental study that looked at using a

mobile application for provider resilience. The intervention started with an educational

session explaining compassion fatigue and to increase awareness of it, then participants

in the intervention arm were instructed on use of the application. This application was

developed by the Department of Defense to aid compassion fatigue in healthcare

providers who treat military personnel. The investigators monitored usage via tracking

software also downloaded. The ProQOL 5 was used pre- and post-intervention to assess

levels of compassion fatigue. Total participants were 25; 16 were in the investigation arm

and nine in the control arm. Results revealed no statistical difference in either arm

however, burnout and STS scores did decrease in both arms most likely due to increased

awareness of compassion fatigue. The researchers point out that this was a pilot study

only and that results should be evaluated accordingly.

The last study that looked at resiliency training for combating compassion fatigue

was Pfaff et al. (2017). They conducted an experimental mixed methods design as a pilot

study to evaluate the effects of a compassion fatigue resiliency program. There were 32

participants enrolled. The intervention was based on the compassion fatigue accelerated

recovery program (ARP) designed by Gentry and colleagues (2007, as cited in Pfaff et

40

al., 2017). The ARP was a 6-week program with classes once a week, see the article for

details about what was included in the program. Quantitative data was collected using the

ProQOL tool pre- and post-program; qualitative data was collected mid and post-program

as focus groups and individual interviews. Twenty-seven completed the program but only

15 completed the post-intervention ProQOL surveys. Only 12 had complete datasets.

Qualitative data entailed three focus groups (n = 12) and individual interviews (n = 8).

The quantitative data revealed no statistically significant changes in mean scores for

compassion fatigue (p = .1) however the scores did decrease for burnout (22.1 to 21.5, p

= .87) and STS (24.8 to 22.7, p = .31). Qualitative data imparted two recurrent themes

“self-reflection and perceived risk of developing compassion fatigue and seeking

personal balance through the use of self-care strategies” (p. 515). This data while not

statistically significant does support resiliency training. The researchers do cautious

interpretation of results due to it being a pilot study and small sample size. As a side note

the authors did show a statistically significant reduction in clinical stress as measured by

the Index of Clinical Stress (p < .005).

Synthesis of these six studies reveals that there were overall no statistically

significant decreases in the compassion fatigue scores but that in most of the study there

were decreases in compassion fatigue (Jakel et al., 2016; Kestler et al., 2020; Klein et al.,

2018; Pfaff et al., 2017; Potter et al., 2013) There was one study that revealed

compassion fatigue scores worsened over time (Pehlivan & Guner, 2020). In Pehlivan &

Guner (2020), where compassion fatigue scores worsened over time this may be because

they were followed the longest (one year) and had the largest number of participants (n =

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125). Two studies followed participants for 6 months (Klein et al.,2016; Potter et al.,

2013), one for 3 months (Kestler et al., 2020) and two only evaluated pre- and post-

intervention (Jakel et al., 2016; Pfaff et al., 2017) Participation numbers ranged from

eight to 125. Five studies involved resiliency training classes (Kestler et al., 2020; Klein

et al., 2018; Pehlivan & Guner, 2020; Pfaff et al., 2017; Potter et al., 2013) while Jakel

and colleagues (2016) used a mobile application. The class setup varied widely between

the authors. Three studies were conducted only as pilot studies (Klein et al., 2016; Pfaff

et al.,2017; Potter et al., 2013). Based on the data, there is evidence to support further

studies using resiliency as an intervention to combat compassion fatigue.

Two articles evaluated the use of debriefing sessions and their impact on

compassion fatigue. In a mixed methods quality improvement study by Zajac et al.

(2017) they explored the usage of debriefing after each patient’s death. This came about

because during the initial investigation of the decreasing patient satisfaction scores it was

discovered that the nurses were suffering from compassion fatigue. A pre-intervention

educational session included information regarding the project, an information sheet, and

the pre-intervention surveys. The intervention was carried out over 3 months; during this

time there were 16 patient deaths and 15 debriefing sessions. Post-intervention surveys

were not matched with pre and only included those nurses that completed pre-

intervention surveys. The quantitative data did not reveal significant differences in

compassion fatigue scores post-intervention. Qualitatively, however, the nursing staff did

report that they did feel that the debriefing sessions were helpful to them.

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In the second article related to debriefings, Arbios et al. (2022) conducted a

quality improvement project to address compassion fatigue in pediatric intensive care

nurses. The cumulative stress debriefings were conducted every month, lasting about an

hour for 6 months. Nurses completed a pre-intervention survey and a 6-month survey.

The sixth-month survey was given also to non-participants to assess barriers to use.

Based on these surveys, the sessions were increased to twice a month. The participants

were then resurveyed at 9 and 12 months. Because there was no identifying data collected

on surveys it is unclear whether the surveyed nurses were the same from previous surveys

leading to the inability to collect any statistical data on the effects the debriefings had on

compassion fatigue. Qualitatively nurses did report that they felt that the sessions were

beneficial to physical and mental well-being.

Yilmaz et al. (2018) conducted a pre- and post-intervention study that examined

the use of multiple nurse-led interventions. Preintervention data was collected using the

ProQOL 4 and the Post Traumatic Growth Inventory. The interventions were then carried

out that included two sessions that entailed didactic information on the topic, background

reading, video demonstrations, exercise, baksi dance and mandala painting techniques,

followed by counseling via a smartphone application for two weeks after the session.

Motivational messages were sent to the nurses daily via this application. The intervention

period ran for 4 weeks. Post-intervention data were collected at 5 weeks. There were 43

participants in the study. Data did reveal a statistically significant decrease in compassion

fatigue with a p-value of < .001, indicating that having nurses trained on multiple

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different types of self-care techniques does have a significant effect on compassion

fatigue.

Meditation was looked at by Hevezi (2016) as a possible intervention to combat

compassion fatigue. She conducted a non-randomized pre- and post-intervention pilot

study with 15 participants. The ProQOL 4 was completed pre-intervention and after a 4-

week intervention trial. There were also four supplemental questions added to collect

qualitative data to the post intervention survey. The interventions were taught during a

one-on-one educational session with participants receiving an educational information

folder and audio CD with three different breathing/meditation choices ranging from 4 to

8 minutes. Participants committed to doing the exercises five times a week for 4 weeks.

The ProQOL 4 was administered before starting intervention and after the intervention in

week 5. The results did show statistically significant decreases in burnout (p = .003) and

STS (p = .0047). The effect size was large at d > .5. Qualitative data collected via the

supplemental questions revealed that the nurses reported lower levels of stress, increased

relaxation effect, and increased feelings of self-compassion. This study does support the

use of meditation as a tool to combat compassion fatigue.

Reiser and Gonzalez (2020) conducted a quality improvement project to increase

self-compassion through toolkits to combat compassion fatigue. The toolkits contained

many resources on mental health coaches, mentorship opportunities, therapies at an

integrative medicine center and health coaches. These toolkits were placed at the nursing

station of two oncology units. Participating nurses completed the ProQOL tool plus other

tools before the toolkits being placed. The second phase of the study looked at barriers to

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using the kits. Data revealed that the tool kits were not felt to be helpful at all. The

qualitative data that they received from the nursing staff revealed that there was very poor

usage of the toolkit. Focus groups were conducted to assess why the toolkits were not

helpful. This revealed complex shifts, understaffing, and patient acuity as reasons for not

using them. During these focus groups the researchers found that nurses did not want

interventions to enhance self-care and compassion satisfaction from their leadership. One

of the things they did want from leadership included respite rooms incorporated into the

facility.

Rajeswari et al. (2020) conducted an experimental pre and posttest interventional

study with participants randomized to either the experimental arm or the control arm.

They were evaluating whether an accelerated recovery program impacted compassion

fatigue stores as measured by the ProQOL 5 tool. There was a total of 120 participants;

60 in each arm. The intervention was an accelerated recovery program (see article for in-

depth details about the program) that was carried out once a week for 5 weeks lasting 90

to 120 minutes and included didactic and experiential training along with audio guidance.

Surveys were done pre-intervention, after training, and then at 3, 6, 9 and 12 months.

Data revealed that the use of an accelerated recovery program does have a statistically

significant result on decreasing burnout scores (p = .001) and STS scores (p = .001); the

two components of compassion fatigue. The mean scores for burnout at baseline was

47.72 and at one year it was 35.6; the STS scores were 46.57 at baseline and 35.57 at one

year. These results indicate that the components of an accelerated recovery program have

lasting effects.

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Wayment et al. (2019) evaluated the effects of a brief “quiet ego” workplace

intervention on compassion fatigue. Quiet ego is a brief cognitive intervention that allows

for reflection and rumination. The goal of this study was to assess the effects of quiet ego

on self-rated health, compassion fatigue, and compassion satisfaction. The total final

sample size was 37. The intervention was taught in a workshop setting with four sessions

conducted every other week lasting 45 to 60 minutes. Results of the study were positive

in that participants employed quiet ego cues many times (88%) when stressed. This study

did find a strong correlation between compassion fatigue levels and general self-reported

health at r = -.35 (p < .05). The results did show a statistically significant reduction in

compassion fatigue (p = .001) and the self-reported health improved (p = .051). This

study highlights the importance of studying compassion fatigue and health complaints

together.

The next two articles discuss the use of camps or retreats to help combat

compassion fatigue and nursing staff. The first article by Lee et al. (2018) came about

due to an investigation that revealed that many nurses working in the burn unit were

leaving due to compassion fatigue. These camps were designed for the victims of burn

injuries, who happen to be children, as a way for them to feel normal again. The burn unit

nurses were invited to act as counselors or chaperones so that they could see the

outcomes of their painful work. Though no statistical data was collected, the authors did

report that the nurses felt they could make peace with their work. Since the program’s

inception 40 nurses and over 220 children have participated. This article highlights the

46

importance of giving nursing staff time to reflect and reconnect with their nursing

purpose.

The second article looked at self-care retreats for pediatric hematology oncology

nurses (Altounji et al., 2012). Though this study did not measure compassion fatigue, the

qualitative data from the retreats revealed that nurses felt revived and rejuvenated, that

their passion for their work was rekindled and that the retreats made them feel

appreciated. What this article and the Lee et al. (2018) article offer is anecdotal evidence

that suggests having some sort of quiet self-care area can help allow nurses to reflect and

reconnect with the reason why they became nurses.

Summary

While there is a plethora of data regarding compassion fatigue, definitions, signs,

and symptoms, defining attributes, consequences and sequela, and interventional studies

of interventions; there is no one way that works for all people to combat compassion

fatigue. This literature review has touched on compassion fatigue in nursing and other

disciplines including, teachers, law enforcement, fire fighters, social workers, lawyers,

and judges. There was information presented on the physical, psychological, and work-

related problems seen with compassion fatigue. Finally, studies were abundant discussed

that looked at interventions tested to assess their effectiveness in combatting compassion

fatigue. Many of these studies do show promise at effectively assisting persons suffering

from compassion fatigue however, there is not enough evidence to point to one specific

intervention. What is noticed is that having a variety of options available in a location

that is easily accessible to people shows the most promise (Copeland, 2018; Rajeswari et

47

al., 2020; Yilmaz et al., 2018). This study seeks to assess compassion fatigue levels and

general health complaints in oncology nurses and explore if there is a correlation between

compassion fatigue scores and general health complaints.

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Chapter 3: Research Method

The purpose of this mixed method convergent concurrent study was twofold. The

quantitative purpose was to examine the relationship between compassion fatigue and

health complaints. The qualitative purpose was to explore nurses’ perceptions of

compassion fatigue. Mixed-methods research was chosen because this methodology

provides quantitative data that may show statistical significance while at the same time

adding the richness and depth of qualitative data that address the lived experiences of

oncology nurses. This chapter includes a description of the study’s methodology,

including the research design and setting, the role of the researcher, recruitment of

participants, data collection procedures, and instrumentation. It also includes the data

analysis plan, threats to validity, issues of authenticity and trustworthiness, and ethical

issues.

Research Design and Rationale

The primary concepts studied in this project were compassion fatigue and general

health. Compassion fatigue is “the negative aspect of the work of caring for others”

(Stamm, 2010, p. 5). General health is defined as “a state of complete physical, mental,

and social well-being and not merely the absence of disease or infirmity” (World Health

Organization, n.d., p. 1). Professional quality of life is “the quality one feels about their

work as a helper” (Stamm, 2010, p. 8). The term helper includes any professional in a

position to help others in times of crisis. There are both positive and negative facets of a

profession that affects a person’s professional quality of life. Positive professional quality

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of life has been termed compassion satisfaction, while negative has been termed

compassion fatigue (Stamm, 2010).

The mixed-methods design that I used was a convergent, concurrent design (see

Gray et al., 2017). This design is beneficial when a researcher wants to confirm findings

within a single study using a single sample. In this design, both quantitative and

qualitative data are collected simultaneously, analyzed separately, and then integrated to

interpret and draw conclusions (Gray et al., 2017). I selected this approach as the best to

answer the research questions because it provides quantitative data that may show

statistical significance while at the same time adding the richness and depth of qualitative

data. Both quantitative and qualitative methods were needed in the study because they

provided a combination of results to answer the research questions, which were as

follows:

RQ1 (qualitative): What are the perceptions of oncology nurses regarding

compassion fatigue?

RQ2 (quantitative What is the correlation between compassion fatigue and

general health complaints in oncology nurses as measured by the ProQOL 5 and the

GBB-8?

H02: There is no correlation between compassion fatigue and general health

complaints.

H12: There is a correlation between compassion fatigue and general health

complaints.

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Setting

This study was an online survey posted to the Oncology Nursing Society

community digest board. The study targeted actively working oncology nurses with at

least 1 year of experience. This forum had the ability to reach over 10,000 oncology

nurses.

The rationale for the choosing mixed-methods design was to allow both

quantitative and qualitative data to be collected within a single sample group. A single

sample group was chosen because the goal of this research was to compare findings about

a topic and avoid adding extraneous variables by having two different samples (see

Creswell & Plano Clark, 2018). Quantitative data and qualitative data were collected at

the same time. Quantitative data were collected via a demographic tool, the ProQOL 5

tool, and the GBB-8 (see Appendix A). Qualitative data were collected via a series of

eight open-ended questions to gather information on compassion fatigue and general

health of oncology nurses. The data were analyzed, separating, and then merged. The

intent of this merger was to expand the understanding of compassion fatigue and general

health in oncology nurses. These results are presented in a comparative joint display (see

figure 2; see Creswell & Plano Clark, 2018).

Role of the Researcher

My roles as the researcher in the study were many. I was the one explaining this

study to the participants and obtaining informed consent via the survey link. I also was

the one collecting and interpreting the data. There was only a very slight risk of

participation bias if any of the local area nurses were part of the Oncology Nursing

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Society, they may recognize my name. To my knowledge, there were no competing

studies through the Oncology Nursing Society. There is no conflict of interest because I

and the Oncology Nursing Society were interested in addressing compassion fatigue and

general health in the oncology nursing staff. No ethical issues were identified.

Methodology

Population

The population for the study was oncology nurses in the United States. Inclusion

criteria included that participants must be 18 years old or older, with at least 1 year of

oncology experience, and actively working full-time or part-time in the oncology setting.

Exclusion criteria were less than 18 years old, with less than 1 year of experience in the

oncology setting, and not currently working in oncology.

Sampling Procedures

The sampling method I used was purposive sampling because I am focusing on a

specific phenomenon in a particular population; compassion fatigue and general health in

oncology nurses (see Gray et al, 2018).

The included sample was used for both the qualitative and quantitative sections.

The sample sizes were different for the two sections. Creswell and Plano-Clark (2018)

noted that having a smaller qualitative sample and a larger quantitative sample helps the

researcher “obtain a rigorous and in-depth qualitative exploration and a rigorous high

power quantitative examination of the topic” (p. 188). Even though the sample was

purposeful, I aimed for at least 55 individuals in the quantitative portion of the study.

This number was decided based on G*Power analysis (see Faul, 2020). G*Power analysis

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revealed a minimum sample size of 42 based on a large effect size (d = .5), a power of

.95, and an alpha error probability of .05. The number 55 was chosen to allow an attrition

rate of 20%. Concerning the qualitative data, there needed to be an adequate sample size

to achieve saturation. Saturation with qualitative data is defined as the point in qualitative

data collection when no new information is being revealed (Rubin & Rubin, 2012). Due

to this, there was no preset minimum number of participants in qualitative data collection

as there was in quantitative data collection to achieve statistical power.

Procedures for Recruitment, Participation, and Data Collection

To recruit participants and obtain a diverse demographic, I posted the study to the

Oncology Nursing Society community digest board and provided information about the

study (see Appendix C). This post reached up to over 10,000 RNs. All eligible

participants could read about the study purpose and decide if they wanted to participate.

There were two different links available, one had the quantitative tools only while the

second one had the quantitative tools and the qualitative questionnaire. Both links were

available as a one or a two and the participant randomly selected a link to complete the

study. Informed consent was implied if the participants completed the questionnaire and

tools.

Instrumentation

Qualitative Components

Qualitative data collection was undertaken via eight open-ended questions with

written responses (see Appendix A). These questions focused on compassion fatigue and

general health. To address threats to validity the same concepts of compassion fatigue

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and general health were addressed in quantitative and qualitative data collection (see

Creswell & Plano-Clark, 2018). To address the issues of authenticity and trustworthiness,

the questionnaires were analyzed and saved by myself only and made available for audit

to the university as warranted.

Quantitative Components

ProQOL 5 Tool.

The ProQOL5 tool was originally developed by Dr. Charles Figley in the late

1980s and has since gone under revision and refinement. The scale measures compassion

fatigue via burnout and secondary traumatic stress and then compassion satisfaction. The

compassion fatigue scale is distinct. I collected data on all three parts of the scale as it is

the best way to support its validity and reliability. Measurement of the ProQOL5 has 30

questions on a Likert scale and the directions on how to score it are in the manual that

accompanies it. The reliability is 0.88. Burnout scores less than 23 are reflective of

positive feelings in the workplace, whereas scores greater than 41 indicate a higher risk

of burnout. A secondary traumatic stress score greater than 43 indicates a high level of

STS and the need for intervention. The two scales, burnout and secondary traumatic

stress, equal the compassion fatigue scale. There is no statistical difference across gender,

age, race, income, years in the current position or field (Stamm, 2010). This tool has

proven both valid and reliable with over 200 published articles and more than 100,000

articles on the internet (see Appendix A for tool and statement of permission).

54

GBB-8.

The GBB-8 was adapted from the GBB-24, a German measure of subjective

health complaints (Kliem et al, 2017). The GBB-8 has eight items rated on a Likert scale

ranging from 0 (not at all) to 4 (very much), indicating how troubling each complaint is

perceived. This adaption was developed and validated in a large population study with

over 2000 participants. The psychometric analyses included confirmation of factor

structure, classical item analysis, and measurement invariance tests. The sample was

deemed to serve as a normal group for the population. To determine construct validity,

correlations with measures of anxiety, depression, alexithymia, and primary care contact

were computed. Analyses revealed a Cronbach’s alpha of .88, the comparative fit index

was .980. This applies to the four-factor model represented in the GBB-8 (i.e.,

exhaustion, gastrointestinal complaints, musculoskeletal complaints, and cardiovascular

complaints). Construct validity of the scale is evidenced by the correlation coefficients of

the GBB-8 total score with depression and anxiety were r = .56. The GBB-8 (see

Appendix A for tool and statement of permission) score also showed high correlations (r

= .44, p < .001) with the number of primary care provider contacts in the previous year,

as well as the number of physician consultations (r = .45, p < .001; see Kliem et al.,

2017).

Demographic Questionnaire.

The third quantitative tool was a basic demographic questionnaire (see Appendix

A) developed by me. I used this questionnaire to collect basic demographic data such as

age, years of nursing experience, years of oncology-specific experience, marital status,

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and gender identification. This was used to establish the population being studied. These

instruments, both quantitative and qualitative, provided sufficient data to answer the

research questions.

Data Analysis Plan

The data were analyzed separately and then merged to answer the research

questions. The data were analyzed quantitatively using SPSS (Version 27) software and

qualitatively by using Saldana’s first- and second-level coding methods. The research

questions were as follows:

RQ1 (qualitative): What are the perceptions of oncology nurses regarding

compassion fatigue?

RQ2 (quantitative What is the correlation between compassion fatigue and

general health complaints in oncology nurses as measured by the ProQOL 5 and the

GBB-8?

H02: There is no correlation between compassion fatigue and general health

complaints.

H12: There is a correlation between compassion fatigue and general health

complaints.

Quantitative Data Analysis Plan

The quantitative analysis plan included the demographic questionnaire to

establish the population, the ProQOL 5 and GBB-8 data. The data were cleansed by

reviewing all the tools to ensure they are filled out. Any quantitative tools not completed

100% were not included in the final data set. All data were entered into SPSS by me.

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Demographic data are displayed as tables to establish the population (see table 1

demographic data; see table 2 demographic data from qualitative subset). Both inferential

and descriptive statistics were used to answer the research questions (Frankfort-Nachmais

et al., 2021).

For RQ2, I used Pearson’s correlation to look at the relationship between the

variables to start to interpret the data and determine if there was a relationship in the data.

Pearson’s correlation was used to determine the strength of that relationship, determining

if we accepted the hypothesis and rejected the null. The p-value was set to p = .025.

Qualitative Data Analysis Plan

Qualitative data were collected through written responses to eight open-ended

questions that explored the perceptions of oncology nurses regarding compassion fatigue

and their general health.

These questions were evaluated and coded for any recurring codes or themes

using Saldana’s (2021) coding methods. First-level coding was conducted by manual in

vivo coding (not the software). This coding method is also known as literal or verbatim

coding and applies to all forms of qualitative research (Saldana, 2021). In vivo coding

helps “to preserve the participants’ meanings and actions” (Chasm, 2014, as cited by

Saldana, 2021, p.14). Second-level coding was undertaken using pattern or thematic

coding (Saldana, 2021). Pattern codes are “explanatory or inferential codes” (Saldana,

2021, p.322). Pattern coding was appropriate in this instance because it condenses a large

amount of information into different analytical units (categories/themes) and looks for

causes and explanations in the data.

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The data were then integrated to draw additional insights into compassion fatigue

and general health in oncology nurses. Using the quantitative results with the qualitative

analysis enhanced understanding and provided insight into the research problem. The

mixed results are presented in a comparison joint display (see figure 2; see Creswell &

Plano Clark, 2018).

Threats to Validity

External threats to validity limit the ability of the results to be generalizable to

other settings and populations (Gray et al., 2017). Some threats that may have been seen

and were addressed in this study were with sampling and attrition rate. Using purposeful

sampling does decrease the possibility of generalizability; however, the sample size was

large enough to ensure that the needed number of participants was above what was

determined by G*Power analysis to achieve statistical results.

Internal validity refers to the degree to which one variable affects the other (Gray

et al., 2017). One threat to internal validity with this study could have been treatment

effect. Participants knew that they were being evaluated for health complaints and

compassion fatigue, and knowing this may have caused them to answer differently;

however, this does not seem to be the case based on the data. Since all questionnaires

were anonymous, there was no risk to the participants based on their results. Attrition

also falls into internal validity and was covered in the discussion on external validity.

Threats to construct validity involve design, measurement, and social interplay

(Gray et al., 2017). To control for these, all definitions were clearly defined, the design

applied to the study, all measurement tools have been thoroughly tested and validated.

58

Threats to statistical conclusion validity include violated assumptions of statistical tests,

low statistical power, and fishing (Gray et al., 2017). To control for this, the statistical

tests that were used had already been determined and discussed; this addressed fishing. A

G*Power analysis was completed to ensure adequate power and a larger sample size was

obtained to allow for a 20% attrition rate and thus maintain statistical power.

Issues of Trustworthiness

Issues of trustworthiness are important to discuss because they can assure that I

have a reliable and valid study. The issues discussed include credibility, transferability,

dependability, and confirmability (Houser, 2018). Credibility was established due to

prolonged time spent with the data and documented in an analytic report. Triangulation

helped maintain credibility by having provided an extensive literature review to support

the study variables. Transferability was established by providing an in-depth description

of the study design, methods, and data for others to replicate the study. Dependability

was exhibited through extensive discussion about the research method and questions,

why they were chosen and how the methods answered the research questions. My

research design and methods were discussed with both quantitative and qualitative

statisticians at Walden University. One aspect of the study results that could be

questioned would be the coding portion of the qualitative data; to ensure dependability

my qualitative data is available for audit by my committee and the institutional review

board (IRB) as applicable or requested. Confirmability is an issue with qualitative data

collection; to help avoid this I used direct quotes from the data. I also used constant

59

comparative methods to ensure I was quoting the data correctly to ensure confirmability

and dependability. I also kept a decision trail to assist with any audit (see Houser, 2018).

Ethical Procedures

Ethics in research are extremely important to discuss. Throughout my entire study

I followed the ethical principles laid out in the Belmont report; respect for persons,

beneficence, and justice (Gray et al., 2017). With regards to respect for persons, all

participation was entirely voluntary and they had the right to withdraw from study, at any

point, without any consequence. No participants were forced or coerced into

participating. Regarding the principle of beneficence, there was no intervention and all

tools and questionnaire were designed to do no harm. Regarding the principle of justice,

all oncology nurses could participate if they met inclusion criteria. All participants were

treated equally and fairly. There were no vulnerable populations in this study. There were

no power relationships involved. There was no personal data noted on any of the tools.

There were no participant’s names on any of the data. All data will be kept secure on a

flash drive that will always remain in my possession or in my home. All data will be kept

for a total of 5 years as per university guidelines and then destroyed. I received

institutional permissions from the university IRB (Approval No. 12-02-22-1041971). All

data will be made available to the university following their guidelines.

Summary

In this chapter, I have discussed the methodology of the proposed research study.

There has been a detailed discussion about the population, the setting, and the sample

size. The various tools that will be used in the study, along with their data collection

60

methods were also discussed. This chapter concluded with a review of threats to internal

and external validity, issues of trustworthiness and ethical considerations.

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Chapter 4: Results

The purpose of this mixed methods convergent concurrent study was twofold. The

quantitative purpose was to examine the relationship between compassion fatigue and

health complaints. The qualitative purpose was to explore nurses’ perceptions of

compassion fatigue. The following research questions and hypotheses were used to guide

this study:

RQ1: What are the perceptions of oncology nurses regarding compassion fatigue?

RQ2: What is the correlation between compassion fatigue and general health

complaints in oncology nurses as measured by the ProQOL 5 and the GBB-8?

H02: There is no correlation between compassion fatigue and general

health complaints.

H12: There is a correlation between compassion fatigue and general health

complaints.

The variables studied were nurses’ compassion fatigue and general health

complaints.

Setting

The setting for this study was an online survey comprised of two different study

links: one with the quantitative survey questions only and a second with both quantitative

tools and the qualitative questionnaire. These links were posted to the Oncology Nursing

Society community digest board once permission was received from the governing

organization. Data collection began on December 5, 2022. This forum reaches over

10,000 oncology nurses across the United States. Since this was an online study, personal

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or organizational conditions that may have affected participation could not be interpreted.

Participants self-selected if they were able to participate or not. Data collection concluded

on December 19, 2022, when the target number of participants of 55 was achieved.

Demographics

Demographic data were collected on a total of 55 participants. Analysis of the

demographic data revealed that 98% of respondents were female (n = 54) and 2% were

male (n = 1). In terms of employment status, 84% worked full time (n = 46), whereas

16% worked part time (n = 9). Data revealed that 76% of the respondents were white (n =

42). Ages ranged from 25 to 65 plus with no respondents under age 25; most respondents

fell into the 45 to 64 age group (n = 30). With regards to years of experience, 45% of the

respondents had been in nursing over 25 years (n = 25); 17 of those respondents had

spent that time working in oncology. See Table 1 for demographic data for the total

sample. The demographic data on the subset of participants who completed the

qualitative questionnaires in presented in Table 2.

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Table 1

Demographic Data (N = 51)

Characteristic n % Age of respondent

25-34 10 18 35-44 9 16 45-54 14 26 55-64 16 29 65+ 6 11

Gender Male 1 2 Female 54 98

Ethnicity Caucasian 42 76 African American 5 9 Hispanic/Latino 2 4 Asian American 2 4 Other 3 6 Prefer not to answer 1 2

Marital status Single 11 20 Married 35 64 Divorced 4 7 Widowed 3 6 Prefer not to answer 2 4

Number of children 0 22 40 1-2 22 40 3-4 9 16 5+ 2 4

Highest level of education Diploma 1 2 Associates 4 7 Bachelors 23 42 Masters 21 38 PhD/DNP 6 11

Nurse of years as a nurse 1-4 4 7 5-9 10 18 10-14 3 6 15-19 8 15 20-24 5 9 25+ 25 46

Number of years as an oncology nurse 1-4 10 18 5-9 11 20 10-14 6 11 15-19 6 11 20-24 5 9 25+ 17 31

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Table 2

Demographic Data From Qualitative Subset (n = 15)

Characteristic n % Age of respondent

25-34 3 20.0 35-44 3 20.0 45-54 2 13.3 55-64 3 20.0 65+ 4 26.7

Gender female 15 100

Ethnicity Caucasian 10 66.7 African American 2 13.3 Asian American 1 6.7 Other 2 13.3

Marital status Single 2 13.3 Married 9 60 Divorced 1 6.7 Widowed 2 13.3 Prefer not to answer 1 6.7

Number of children 0 5 33.3 1-2 7 46.7 3-4 2 13.3 5+ 1 6.7

Highest level of education Associates 1 6.7 Bachelors 6 40 Masters 7 46.7 PhD/DNP 1 6.7

Number of years as a nurse 1-4 1 6.7 5-9 2 13.3 10-14 1 6.7 15-19 3 20.0 20-24 1 6.7 25+ 7 46.7

Number of years as an oncology nurse 1-4 3 20.0 5-9 2 13.3 10-14 4 26.7 15-19 1 6.7 25+ 5 33.3

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Data Collection

Data were collected via SurveyMonkey on a total of 55 participants; all 55

completed the quantitative data, whereas 15 completed the qualitative data. I posted two

survey links on the Oncology Nursing Society community digest board that had the

potential to reach over 10,000 oncology nurses. Participants self-selected one of the two

links to complete the surveys. One link contained only the quantitative tools, whereas the

other link had both quantitative tools and the qualitative questionnaire. Thirty-nine

participants self-selected the first link which was only quantitative tools while 16 selected

the second link that had both quantitative tools and the qualitative questionnaire. Only 15

of the 16 participants that selected Link 2 completed the qualitative questions; one

participant did not answer any of the qualitative questions but did complete the

quantitative portion and was therefore only included in the quantitative data analysis.

The study was posted on December 5, 2022, and stayed open and available on the

forum until the desired number of participants was reached (N = 55). It took 14 days to

reach that number. Data were recorded through SurveyMonkey. All data were collected

according to the plan laid out in Chapter 3; there were no variations. There were also no

unusual circumstances encountered with collecting the data. The study closed on

December 19, 2022.

Data Analysis

The collected data were exported from SurveyMonkey to Microsoft Excel where

they were cleaned before being transferred to SPSS 27 for data analysis of the

quantitative tools. Of the 55 quantitative surveys collected, four were incomplete; three

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were missing data to complete scoring of the compassion fatigue levels, and one was

missing one question on the health complaint tool. These four surveys were not used in

the final data analysis for compassion fatigue and general health complaints; they were

only used for demographic purposes.

Qualitative data analysis was undertaken through Saldana’s (2021) first- and

second-level coding. The first-level coding techniques that was used is called in vivo

coding also known as “verbatim coding” (Saldana, 2021, p.137). This method uses words

or short phrases from the actual participants responses. This allowed me to pull the actual

words and phrases that stood out. After first-level coding was completed, I waited a few

days before going back to complete second-level coding. The second-level coding I used

was pattern coding, also by Saldana (2021). Pattern coding is used to look for inferential

codes in the data to develop a theme. In this instance, several different themes presented

themselves in the data: fatigue, overwhelming, irritability, anxiety, depression, muscle

pain and body aches, sense of purpose, and fulfillment. See Table 3 for a list of the

qualitative themes. See Figure 1 for a visual representation of these themes; the word

sizing is representative of how often the words were mentioned in the qualitative data.

There were no significantly discrepant cases in the data.

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Table 3

Qualitative Themes

Qualitative themes Number of times mentioned Fatigue 9 Overwhelming 3 Irritability 2 Sense of purpose 1 Fulfillment 1 Muscle pain and body aches 4 Anxiety 2 Depression 3

Figure 1

Qualitative Themes

Results

Results of the study will be discussed in the following section. As this was mixed

methods research study, I will discuss the results of the research questions separately and

then discuss how the data merges to support each other.

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Qualitative Results

My first research question was “What are the perceptions of oncology nurses

regarding compassion fatigue?” I had a total of 14 respondents answer this question.

First-level coding as described above revealed that all the participants who responded to

the question believed that it was a very real phenomenon. Respondent 1 stated that she

felt it was “huey [sic] until it started to affect me.” Respondent 8 felt that it was “giving

more of yourself than you can refill.” Based on respondent comments, they felt that

compassion fatigue was a real phenomenon that needs to be addressed.

Second-level coding revealed that compassion fatigue is a “very real

phenomenon” (Respondent 9), where nurses are “losing interest and joy in caring for

patients” (Respondent 2). Respondent 1 stated that “it’s more of a chore to interact with

people anymore and to go work.” Respondent 3 stated that compassion fatigue “is a very

real, multi-factorial experience,” while Respondent 5 called it a “genuine ailment.”

Respondent 7’s perception of compassion fatigue was that it is “a real issue that happens

often and quickly with a certain patient population.” Respondent 8’s perception of

compassion fatigue was that it was “giving of yourself more than you can refill.” She

went on to state that “the compassion is gone and despite the person’s desire to give

compassion, their tank is just empty and they have nothing left to give.” Respondent 14’s

perception of compassion fatigue was one of “burnout; numbness.” She also stated that

she “believes that compassion fatigue negatively impacts health.” Respondent 16’s

perception of compassion fatigue is one of “hopelessness and detachment.”

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Additional data were collected from participants about whether they felt they

suffered from compassion fatigue and why they felt that way. Forty-seven percent of

participants reported feeling that they did suffer from compassion fatigue, while 40%

stated that no they did not, and 13% said that sometimes they thought they suffered from

it (n = 7, 6, and 2, respectively). Among the seven respondents who reported suffering

from compassion fatigue, Respondent 1 wrote that she knew she suffered from

compassion fatigue because “I cry at the drop of a hat, could spend all day in bed, it’s

more of a chore to interact with people anymore and go to work.” Respondent 13

reported knowing that she suffered from compassion fatigue because she “felt she had

nothing left to give to anyone.” One of the six participants who reported that they did not

suffer from compassion fatigue, Respondent 4, stated that “I meditate, do yoga and pray

and I find those activities help me stay focused.” Respondent 14 also replied “no” to

feeling like she suffered from compassion fatigue and as to why she stated “I feel

fulfilled and energized by my work.” See Table 4 for details of this data.

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Table 4

Do You Believe You Suffer From Compassion Fatigue and Why Do You Think That?

Do you believe you suffer from compassion fatigue? Why do you think that? Yes (n = 7) “Cried” (1)

“Stay in bed” (1) “Nothing left to give” (13) “Chore to interact with people” (1) “Do not feel as caring” (3) “Not as excited about nursing” (6)

No (n = 6) “Pray” (4) “Meditate” (4) “Practice yoga” (4) “Work makes me feel fulfilled” (6) “Work makes me feel energized” (14) “Work keeps me focused” (4)

Note. The number in parentheses is the respondent who stated that response.

In summary, the answer to the first research question was that oncology nurses do

believe compassion fatigue is a very real phenomenon that needs to be addressed. They

also believe it is exhibited by multiple factors including fatigue, irritability, anxiety, and

depression. They also stated that nurses exhibiting compassion fatigue are physically

exhausted and mentally drained, overwhelmed, and exhibit a lack of interest in their

patients. Respondent 7 stated that nurses exhibiting compassion fatigue seemed to be just

“going through the motions.”

Quantitative Results

Quantitative data analysis was undertaken to explore a correlation between

compassion fatigue levels and general health complaints. The research question answered

here was “What is the correlation between compassion fatigue and general health

complaints in oncology nurses as measured by the ProQOL 5 and the GBB-8?" GBB-8

scores general health complaints on a Likert scale from 0 (not at all) to 4 (very much).

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There are four symptom clusters measured: exhaustion, gastrointestinal complaints,

musculoskeletal complaints, and cardiovascular complaints (see Kliem et al., 2017).

Bivariate correlation analysis was done on each of these four symptom clusters

against burnout and STS, the two components of compassion fatigue as measured by the

ProQOL 5 (Stamm, 2010). G*Power analysis revealed that the minimum number of

participants needed for a large effect size (d = .5), a power of .95 and an alpha error

probability of .05 was 42. A total of 55 participants completed the tools, but four of these

were not fully completed and therefore not utilized in the final data analysis. The final

number for data analysis was 51.

In order to answer the research question, four separate correlational tests were run

for each of the different system clusters mentioned above.

Exhaustion

A Pearson’s correlational analysis was conducted in order to determine if there was a

statistically significant relationship between exhaustion, burnout, and secondary

traumatic stress (see Table 5). Results revealed that there was a medium but statistically

significant positive correlation between exhaustion, burnout, and STS (r = .613, n = 51, p

= .000; r = .521, n = 51, p = .000, respectively).

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Table 5

Pearson Correlation Exhaustion, Burnout, and STS (N = 51)

EXH Variable Pearson correlation Sig. (2-tailed)

BOLEVEL .613** 0.000 STSLEVEL .521** 0.000

Note. BOLEVEL = burnout level; EXH = exhaustion; STSLEVEL = secondary traumatic

stress level.

** Correlation is significant at the 0.01 level (2-tailed)

Gastrointestinal Complaints

A Pearson’s correlational analysis was conducted in order to determine if there

was a statistically significant relationship between gastrointestinal complaints, burnout,

and secondary traumatic stress (see Table 6). Results revealed that there was a small but

statistically significant positive correlation between gastrointestinal complaints and STS

(r = .321, n = 51, p = .022) but not with burnout (r = .187, n = 51, p = .189).

Table 6

Pearson Correlation Gastrointestinal Complaints, Burnout, and STS (N = 51)

GI Variable Pearson correlation Sig. (2-tailed)

BOLEVEL .187 .189 STSLEVEL .321* .022

Note. BOLEVEL = burnout level; GI = gastrointestinal complaints; STSLEVEL =

secondary traumatic stress level.

* Correlation is significant at the 0.05 level (2-tailed)

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Musculoskeletal Complaints

A Pearson’s correlational analysis was conducted in order to determine if there

was a statistically significant relationship between musculoskeletal complaints, burnout,

and secondary traumatic stress (see table 7). Results revealed that there was a small but

statistically significant positive correlation between musculoskeletal complaints and

burnout (r = .294, n = 51, p = .036) but not with STS (r = .199, n = 51, p = .163).

Table 7

Pearson Correlation Musculoskeletal Complaints, Burnout, and STS (N = 51)

MSCO Variable Pearson correlation Sig. (2-tailed)

BOLEVEL .294 .036* STSLEVEL .199 .163

Note. BOLEVEL = burnout level; MSCO = musculoskeletal complaints; STSLEVEL =

secondary traumatic stress level.

* Correlation is significant at the 0.05 level (2-tailed)

Cardiovascular Complaints

A Pearson’s correlational analysis was conducted in order to determine if there

was a statistically significant relationship between cardiovascular complaints, burnout,

and secondary traumatic stress (see Table 8). Results revealed that there was a small but

statistically significant positive correlation between cardiovascular complaints and STS (r

= .370, n = 51, p = .007) but not with burnout (r = .212, n = 51, p = .135).

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Table 8

Pearson Correlation Cardiovascular Complaints, Burnout, and STS

CVCO Variable Pearson correlation Sig. (2-tailed)

BOLEVEL .212 .135 STSLEVEL .370** .007

Note. BOLEVEL = burnout level; CVCO = gastrointestinal complaints; STSLEVEL =

secondary traumatic stress level.

** Correlation is significant at the 0.01 level (2-tailed)

Based on the above analysis the null hypothesis would be rejected. There were

statistically significant relationships seen with the compassion fatigue scales in reference

specifically to burnout in relationship to exhaustion and musculoskeletal complaints (p =

00 and p = .036, respectively) but not with GI complaints or cardiovascular complaints (p

= .189 and p = .135, respectively). Secondary traumatic stress levels were statistically

significant with exhaustion, gastrointestinal complaints, and cardiovascular complaints (p

= .000, p = .022, and p = .007, respectively) but not with musculoskeletal complaints (p =

.163).

Mixing the Data

The merged results are shown in a joint display (see Figure 2) below but will be

discussed narratively for interpretation. In the quantitative correlational data, there is a

moderate statistically significant positive relationship between exhaustion and burnout

and STS; the qualitative data also reflects this. When respondents were asked what they

felt compassion fatigue looked like they responded with “physically exhausted,”

“mentally drained,” “tiredness,” and “nothing left to give.” In the relationship between

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musculoskeletal complaints and burnout and STS there is a positive correlation with both

burnout and STS (r = .294, r = .199, respectively); While this relationship is a small to

weak, respectively, there is a statistically significant relationship with burnout (p = .036).

The qualitative data portrays a stronger picture. When participants were asked about their

general health over the last 6 months, four out of the seven participants who stated that

they did suffer from compassion fatigue, listed their health complaints as increasing

muscle pain and body aches and fatigue. The specific question regarding health

complaints was open-ended and did not ask about specific ailments.

Figure 2 Joint Display of Mixed Results

Note. BOLEVEL = burnout level; EXH = exhaustion; STSLEVEL = secondary traumatic

stress level; MSCO = Musculoskeletal complaints

Even though there was quantitative data indicating a statistically significant

relationship between cardiovascular complaints and STS levels there were no qualitative

responses regarding the two symptoms that fell into that symptom cluster; dizziness and

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palpitations. There was also a statistically significant relationship between

gastrointestinal complaints and STS levels however there was no qualitative data

responses with regards to the symptoms of a stomach ache and feeling bloated that the

GBB-8 uses.

Evidence of Trustworthiness

Evidence of trustworthiness of qualitative data is met through credibility,

transferability, dependability, and confirmability (Houser, 2018). Credibility of this study

was maintained by careful review of the qualitative data. There was extra time spent on

the data; after first-level coding I waited a few days before doing second-level to ensure I

agreed with the first-level coding. Another way I ensured credibility was to use direct

quotes from participants, as seen previously in this chapter. Triangulation also supports

credibility of this study as I had an extensive literature review to support the study

variables and concepts along with the quantitative tools and the qualitative questionnaire.

Transferability was established through ensuring I had detailed documentation of the

study design, methods, and procedures and a comprehensive review of the study variables

and concepts.

My design and methods were discussed at length with both quantitative and

qualitative statisticians at Walden University ensuring dependability of my data along

with providing a detailed report of my research methods. Confirmability of the qualitative

data were met through journal notes that helped me to stay focused on the coding process

along with constant comparative checking. These notes are available upon request to my

dissertation committee or the IRB.

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Threats to Validity

Threats to external validity were minimized in that my sample size was greater

than what was determined by G*Power analysis to meet statistical significance (N = 55).

One threat to internal validity that was previously noted was the possibility of treatment

effect. Participants knew that they were being evaluated for compassion fatigue and

health complaints however this did not seem to affect responses, 47% said that they

thought they did suffer from compassion fatigue while 40% responded that they did not,

13% said that they thought sometimes they suffered from compassion fatigue (n = 7,6,2,

respectively). Attrition also is an internal validity threat and was met by having a total

number of responses (N = 55) which is higher than what G*Power analysis revealed

would be needed (n = 42) to achieve statistical power.

Threats to construct validity were also met in that all definitions and procedures

were clearly defined. I did not deviate from the statistical procedures and testing that

were outlined prior to data collection and analysis. Though I had 55 participants, only 51

had completed the quantitative tools at 100%, the remaining four participant responses

that were incomplete, were used for demographic data only.

Another potential threat to validity that was controlled for was that the

participants self-selected their participation link; this ensured that participation in the

study was random in the two arms (Quantitative only vs. Quantitative with Qualitative).

The study links were available for up to 10,000 oncology nurses through the Oncology

Nursing Society community digest board. The data were collected through

SurveyMonkey, this way the data could not be altered by myself. Since the study was

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conducted online and not as independent interviews or focus groups there was no risk of

the me inflicting my own views upon the responses from the participants; thus,

decreasing the risk of researcher influence. All these above-mentioned items ensure that I

have a valid and reliable study.

Summary

According to the qualitative data that was collected from 15 oncology nurses, the

perception of compassion fatigue is that it is a very real phenomenon that is characterized

by feelings of exhaustion, tiredness, being mentally drained, having increased muscle

aches and pains, along with an increase in irritability, anxiety, and depression. Forty-

seven percent of participants felt that they did in fact suffer from compassion fatigue.

Quantitative data was measured using the ProQOL 5 tool and the GBB-8. The

ProQOL 5 measures compassion fatigue with two separate and distinct scales, burnout,

and secondary traumatic stress. Data analysis revealed statistically significant positive

correlations between burnout with regards to exhaustion and musculoskeletal complaints

(p = .000 and .036, respectively). Statistically significant positive correlation results were

found between STS with regards to exhaustion, gastrointestinal complaints, and

cardiovascular complaints (p = .000, .022, and .007, respectively). Based on these testing

results we would reject the null hypothesis; there are statistically significant correlations

with compassion fatigue and general health complaints in oncology nurses.

In summary, this chapter covered the study setting, demographic data of

participants, and data collection methods. It also covered data analysis and results along

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with issues of trustworthiness and validity. The next chapter will cover interpretation of

findings, limitations to the study, recommendations, and implications.

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Chapter 5: Discussion, Conclusions and Recommendations

The purpose of this mixed methods study was twofold: to investigate the

perceptions of oncology nurses regarding compassion fatigue and to evaluate for

correlation between compassion fatigue levels and general health complaints in oncology

nurses. The study was conducted because research has shown that oncology nurses may

be at a higher risk of developing compassion fatigue due to the nature of their profession

(Kohli & Padmakumari, 2020; Reiser & Gonzalez, 2020).

Key findings from this study include that oncology nurses do believe compassion

fatigue is a very real phenomenon characterized by feelings of mental and physical

exhaustion, anxiety, depression, and irritability. The qualitative responses from oncology

nurses revealed they felt this way because they have “cried,” “stayed in bed,” and “felt it

was a chore to interact with people.” Quantitative data revealed a statistically significant

positive correlation with burnout and secondary traumatic stress (which are the two

components of compassion fatigue) and exhaustion (r = .613, p = .000, and r = .521, p =

.000, respectively). There was also a positive statistically significant correlation between

burnout and musculoskeletal complaints (r = .294, p = .036). Other positive statistically

significant correlations were seen in secondary traumatic stress scores with regards to

gastrointestinal complaints and cardiovascular complaints (r = .321, p = .022, and r =

.370, p = .007, respectively).

Based on these results, the null hypothesis would be rejected because the data do

support the findings of statistically significant correlations between compassion fatigue

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and general health complaints in oncology nurses. When the data are merged, the

qualitative data support and confirm the quantitative data (see Figure 2).

Interpretation of Findings

Interpretation of the findings from this study confirm previous research that

indicates oncology nurses may be a higher risk of compassion fatigue. Ortega-Campos et

al. (2020) conducted a systematic review and meta-analysis that included 900 oncology

nurses with 60% of them reporting moderate to high levels of compassion fatigue. My

study, of 51 oncology nurses, confirmed this finding with 56% of participants reporting

moderate to high levels of compassion fatigue.

Another finding that this study confirmed was that there are general health

complaints associated with compassion fatigue. In a qualitative study, Wentzel et al.

(2019) found that oncology nurses defined one of the symptoms of compassion fatigue as

emotional exhaustion. Current findings from my studies also confirm this as participants

reported symptoms that oncology nurses exhibited that suffered from compassion fatigue

to look like being “physically exhausted” and “mentally drained.”

An area of knowledge that may be extended by my study is that oncology nurses

are becoming more aware of compassion fatigue and know that it needs to be addressed

so that as a profession, oncology nursing does not continue “to lose too many good

nurses” as Respondent 14 put it. Participant 6 also stated that “employers really need to

take notice and DO SOMETHING to help nurses.” This leads to how Pender’s health

promotion model can help to combat compassion fatigue.

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The central construct associated with Pender’s health promotion model is self-

efficacy (Pender, 2011). The participants in the study do realize that compassion fatigue

is a real phenomenon that needs to be addressed; however, according to qualitative

responses, they are putting all of the responsibility for addressing it on administration.

Respondent 6 responded as stated above, and Respondent 14 stated that “it is an area that

needs attention. We are at risk to lose too many good nurses to compassion fatigue and

burnout.” Nurses do need to act themselves and not rely on administration to take action;

hence, self-efficacy.

Pender’s (2011) health promotion model has several assumptions and

propositions that play a role in changing behavior. This study indicates that oncology

nurses do believe compassion fatigue is a real problem and that it needs to be addressed.

One of the assumptions of Pender’s health promotion model is that people will seek to

change if they believe it will have a positive impact on their health. By increasing

awareness of compassion fatigue and the negative health complaints that are correlated

with it, nurses can take steps to combat it and improve their overall health. This ties to

one of the model’s propositions that people will commit to engage in behaviors if they

anticipate personal valued benefits (Pender, 2011). Publishing the findings from this

study can show nurses the role that compassion fatigue plays in their health and take

steps to change behaviors. This aligns with my conceptual model in that one of my social

contexts was to effect change in levels of compassion fatigue, thus impacting oncology

nurses physical and mental health. With these data, I can increase awareness of the

problem of compassion fatigue and the role it plays on a nurse’s health.

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Limitations

There are a few limitations to the study. One limitation is that only oncology

nurses were targeted, which can affect the generalizability of the study. To decrease the

risk of researcher bias, direct quotes from participants were used from the qualitative

questionnaires, which supports the credibility of this study. Another limitation to the

study is that most participants were female (98%); however, this is somewhat

representative of the nursing workforce as, according to the American Nurses

Association, 87% of nursing is female (Haines, 2022).

Recommendations

The results of this study support the assertion that compassion fatigue is a very

real problem in oncology nurses and that there are positive correlations between

compassion fatigue and general health complaints in oncology nurses. One

recommendation based on this data would be to look at various easy-to-use interventions

to target compassion fatigue that could be implemented in the workplace. Some of these

that have been covered in Chapter 2 and that have shown positive results include

debriefing sessions (Arbios et al, 2020; Zajac et al., 2017), compassion rounds (Shingler-

Nace et al., 2018), knitting (Anderson & Gustavson, 2016) and self-care retreats (Altounji

et al., 2012).

Another recommendation is that facilities that employ oncology nurses should

educate managers on how to identify nurses suffering from compassion fatigue and to

intervene. Unfortunately, this study did not ask participants for recommendations on this.

Further research in this area is needed to assist oncology nurses in understanding and

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combating compassion fatigue; assist management in recognizing and intervening with

compassion and finding ways to implement interventions into the workplace.

Implications

Implications for this study are numerous. The study does support that oncology

nurses are at higher risk of compassion fatigue and that compassion fatigue is positively

correlated with general health complaints. Currently, nursing is in a critical shortage and

oncology nurses as a specialty are not excluded from this (Haines, 2022). According to a

report from the Department of Health and Human Services, it is estimated that by the

year 2023 the health care industry will be over 100,000 nurses short to meet growing

demands (Haines, 2022).

In order to impact positive social change, compassion fatigue must be addressed

to help with retention of nurses. As pointed out above, nursing is already experiencing

significant shortages, and this is only projected to get worse (Haines, 2022). According to

a study by Lee et al. (2018) that evaluated nursing turnover at one Southern California

Magnet hospital, they found that in 2015 the turnover rate related to compassion fatigue

was 17.2%. Wells-English et al. (2019) evaluated the levels of compassion fatigue and

nurses’ intent to leave the nursing field, discovering that higher levels of compassion

fatigue indicated an increased intent of nurses to leave the field. This is made evident in

this study with the statement by Respondent 6 who thought she “would retire at 72 but

even now considering retiring early.” She was one that responded “yes” to the question

asking if she suffered from compassion fatigue.

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Pender’s health promotion model is a way for people to change behavior and

promote healthy behaviors (Pender, 2011). I had chosen this model because compassion

fatigue is a health problem that has adverse health effects, including but not limited to

headaches, gastrointestinal problems, depression, anxiety, and fatigue (see Harris &

Griffin, 2015). Addressing compassion fatigue may have a positive effect on nurses’

mental and physical health. Nurses need to be aware of what compassion fatigue is and

how they can promote behavioral changes to combat compassion fatigue.

Conclusion

Compassion fatigue is real and needs aggressive intervention to prevent and

combat it. This research, as with previous studies by Kohli and Padmakumari (2020) and

Reiser and Gonzalez (2020), indicated that oncology nurses are at a high risk for

compassion fatigue. Research also reveals that compassion fatigue does lead to turnover

(Wells-English et al., 2019), which will contribute to the growing nursing shortage.

According to the latest data, which is pre-pandemic, by the year 2030, the projected

demands for nurses will be 3,154,218, whereas the actual nurses working in the field are

predicted to be at 3,047,530; that leaves a shortage of 106,688 nurses (Haines, 2022). The

pandemic has most likely made this number much larger. If compassion fatigue is

addressed and combated, we may be able to retain more nurses in the field and positively

affect this shortage.

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Appendix A: Data Collection Tools

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Qualitative Data Collection Tool

1. How do you feel your profession as an oncology nurse affects you? 2. What is your perception of compassion fatigue? 3. Can you tell me what you believe compassion fatigue looks like? 4. Can you tell me if you believe suffer from compassion fatigue and why? 5. Tell me how your general health has been the last 6 months? 6. Have you noticed any changes in your general health complaints? If so, what do

you feel is attributing to this? 7. What are your thoughts about compassion fatigue and general health? 8. Please tell me anything else you feel is important to know about compassion

fatigue and the general health of an Oncology nurses?

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Appendix B: Socioecological Framework

Note: Socio-Ecological Framework from Plano Clark, V. L., & Ivankova, N. V. (2016).

Mixed methods research: A guide to the field. Sage. Copyright 2016 Sage Publication.