Summative Assignment: Critique of Research Article

ghostrider84

Stress_and_Health_in_Nursing_Students__The_Nurse.6.pdf

Home >Nursing homework help >Summative Assignment: Critique of Research Article

D ow

nloaded from

https://journals.lw w .com

/nursingresearchonline by

B hD

M f5eP

H K av1zE

oum 1tQ

fN 4a+kJLhE

ZgbsIH o4X

M i0hC

yw C X 1A

W nY

Q p/IlQ

rH D 30z+H

3cjN B C m X A B yX

zS 0gR

5h1lxV TxV

A U D W /aLLQ

H D x8=

on 03/26/2020

Downloadedfromhttps://journals.lww.com/nursingresearchonlinebyBhDMf5ePHKav1zEoum1tQfN4a+kJLhEZgbsIHo4XMi0hCywCX1AWnYQp/IlQrHD30z+H3cjNBCmXAByXzS0gR5h1lxVTxVAUDW/aLLQHDx8=on03/26/2020

Stress and Health in Nursing Students The Nurse Engagement and Wellness Study Hector A. Olvera Alvarez ▼ Elias Provencio-Vasquez ▼ George M. Slavich ▼ Jose Guillermo Ceden~o Laurent ▼ Matthew Browning ▼ Gloria McKee-Lopez ▼ Leslie Robbins ▼ John D. Spengler

Background: Evidence suggests that behavioral, social, and environmental factors may modify the effects of life stress on health and performance of new nurses as they transition to hospitals.

Objectives: The aim of this study was to describe the methods of a project designed to investigate the role of social, behavioral, and environmental factors in modifying the adverse effects of stress on new nurses and to discuss demographic, health, and life stress characteristics of the cohort at baseline.

Methods: A prospective cohort design was used to conduct a comprehensive assessment of health endpoints, life stress, behaviors, personal traits, social factors, indicators of engagement and performance, and environmental exposures in nursing students. Adjusted odds ratios and analyses of covariance were used to examine associations between these factors at baseline.

Results: Health indicators in the cohort were comparable or better than in the broader United States population, and lifetime stress exposure was lower than among students from other majors. Exposure to more lifetime stressors was associated with greater risk for various health conditions, including hypertension, diabetes, and depression. Conversely, better social, environmental, behavioral, and personal profiles were associated with protective effects for the same health conditions.

Discussion: These data comprehensively summarize the lives of predominately Hispanic nursing students and highlight risk and resilience factors associated with their health and well-being. The findings are timely, as the nursing field diversifies in preparation to care for a diverse and aging population. Comprehensively assessing stress–health relationships among student nurses ought to inform the policies, practices, and curricula of nursing schools to better prepare nurses to thrive in the often-strenuous healthcare environment.

Key Words: childhood adversity � environmental exposure � health disparities � inflammation � mental health Nursing Research, November/December 2019, Vol 68, No 6, 453–463

A s the largest group of healthcare professionals in the world, nurses play a far-reaching role in healthcare sys- tems. In hospitals, direct patient contact places nurses

in a position to directly affect the outcomes, safety, and satis- faction of patients. However, chronic psychosocial stress can negatively affect the capacity of nurses to provide quality care

by increasing risk for medical error (Elfering, Semmer, & Grebner, 2006) and reducing job satisfaction (Roberts & Grubb, 2014). Nurses experience stress as a result of demand- ing work responsibilities, long shifts, frequentlychangingroles (Moustaka & Constantinidis, 2010), and—in some instances— incivility between coworkers (Oyeleye, Hanson, O’Connor, & Dunn, 2013). This type of frequent and prolonged stress expo- sure can cause biological deterioration (i.e., allostatic load) that increases individuals’ susceptibility to numerous health problems and cognitive decline (McEwen, 1998).

Among nurses, work-related stress has been associated with health problems such as obesity, cardiovascular disease, and Type 2 diabetes (McNeely, 2005), as well as with greater risk for depression, burnout, and suicidality (Karasek & Theorell, 1990). Chronic stress can also affect cognitive function, in- cluding attention and memory, which among nurses could increase their risk of committing medication errors, failing to recognize life-threatening signs and symptoms, or missing other critical patient safety issues (Karimi, Adel-Mehraban, & Moeini, 2018; McEwen, 1998). Stress also increases turnover (i.e., quitting current job) and attrition (i.e., quitting nursing) (Hayes et al., 2012; Shader, Broome, Broome, West, & Nash,

Hector A. Olvera Alvarez PhD, is Associate Professor, School of Nursing, Univer- sity of Texas at El Paso. Elias Provencio-Vasquez PhD, RN, FAAN, FAANP, is Dean, College of Nursing, University of Colorado, Aurora. At the time this research was completed, he was Dean, School of Nursing, University of Texas at El Paso. George M. Slavich, PhD, is Associate Professor, Cousins Center for Psychoneu- roimmunology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles. Jose Guillermo Ceden~o Laurent, PhD, is Research Associate, T. H. Chan School of Public Health, Harvard University, Boston, Massachusetts. Matthew Browning, PhD, is Assistant Professor, Department of Recreation, Sport and Tourism, University of Illinois, Urbana-Champaign. Gloria McKee-Lopez, PhD, RN, is Associate Professor, School of Nursing, Uni- versity of Texas at El Paso. Leslie Robbins, PhD, APRN, FAANP, ANEF, is Associate Professor, School of Nursing, University of Texas at El Paso. John D. Spengler, PhD, is Professor, T. H. Chan School of Public Health, Harvard University, Boston, Massachusetts.

DOI: 10.1097/NNR.0000000000000383

Nursing Research www.nursingresearchonline.com 453

2001), particularly among new nurses (Blythe et al., 2008; Rudman, Omne-Ponten, Wallin, & Gustavsson, 2010), which in turn reduces the effectiveness and productivity of under- staffed nursing teams (Newman, Maylor, & Chansarkar, 2001; Squillace, Bercovitz, Rosenoff, & Remsburg, 2008).

New nurses appear to be particularly vulnerable to stress during their transition from nursing school to the work envi- ronment (Fink, Krugman, Casey, & Goode, 2008). During the first 2 years after graduation, 34% of nurses change jobs, with stress being among the more frequent reasons for leaving (Kovner, Brewer, Fatehi, & Jun, 2014; Kovner & Djukic, 2009). Studies have also reported that professions that require continu- ous human contact, such as nursing, are susceptible to stress and burnout even before employment (Moreira & Furegato, 2013; Rudman & Gustavsson, 2012). Nursing students, for example, encounter stressors unique to the nursing curriculum, such as clinical training (Pulido-Martos, Augusto-Landa, & Lopez-Zafra, 2012), and experience more stress from experiences com- mon to other college students, such as test anxiety (Turner & McCarthy, 2017). The need to balance financial, family, and other life issues, along with academic demands, can also lead to severe stress among students. At this time, however, little is known about how stress experienced in nursing school is associated with susceptibility to stress in the work environment. A better understanding of how students experi- ence stress could improve the performance and well-being of new nurses and, ultimately, the health of their patients.

The effect that stress has on the health and performance of nursing students can be mediated by other life stressors, as well as personal, behavioral, social, and environmental fac- tors that are experienced during school or earlier in life. For instance, the type, number, and severity of stressors experi- enced during childhood appears to increase the sensitivity and inflammatory response to stress experienced later in life (Nusslock & Miller, 2016), which can in turn elevate risk for depression, cardiovascular disease, and hypersensitivity to chemicals and air pollutants (Bell, Baldwin, Russek, Schwartz, & Hardin, 1998; Black, Okiishi, Gabel, & Schlosser, 1999; Olvera Alvarez, Kubzansky, Campen, & Slavich, 2018). Person- ality might also modify the stress experience of nursing stu- dents (Aldwin, Spiro, Levenson, & Cupertino, 2001; Judge & Ilies, 2002). Behaviors such as sleep, diet, and physical activity lessen or amplify the effect of stress on health as a result of in- flammation in the body (Cecil, McHale, Hart, & Laidlaw, 2014; Gleeson et al., 2011; Minihane et al., 2015). The amount and type of social support (Thoits, 2011) and attachment (Mattanah, Lopez, & Govern, 2011) have also been associated with protec- tive health outcomes. Although research on stress among nurses and nursing students is abundant, no studies have examined how behavioral, social, and environmental factors interact in nursing students. This has occurred despite the fact that comprehensive assessments of how these multilevel and multidimensional factors interact could help us prepare the

next generation of nurses to thrive and deliver high-quality care in often-challenging work environments.

The Nurse Engagement and Wellness Study (NEWS) ad- dresses these important issues by examining how behavioral, social, and environmental factors interact to modify the effects of stress on the health and performance of nursing students. NEWS also seeks to investigate how the stress experience in nursing school transcends into the professional work environ- ment. In this first article on NEWS, we provide an overview of the study design and describe key associations between stress and health-related characteristics in the cohort at baseline.

METHODS

Study Design

The NEWS is a prospective cohort study of nursing students and early career nurses directed by a team of nursing, social, and environmental health researchers. Data and biological samples are stored at the Biobehavioral Research Laboratory at University of Texas at El Paso. The study was approved by in- stitutional review boards at University of Texas at El Paso (857149-1) and Harvard University (16-0080).

Sampling

Female and male individuals between 18 and 55 years old, enrolled in the bachelor of science in nursing (BSN) program at the University of Texas at El Paso, were eligible to partici- pate. Participants were recruited via e-mails, posters, flyers, me- dia outlets (e.g., magazine, newsletter), and in-class information sessions. No member of the research team was an instructor in the BSN program during the study. Interested students were asked to visit the laboratory where they provided written in- formed consent. Participants who dropped out of the BSN program, transferred to another university, or failed to graduate from the program were removed from the sample.

Data Collection

Data were collected at three time points. Baseline measure- ments were conducted within 6 months of students entering the BSN program. The first follow-up was within 4 months prior to graduation from the BSN program. Finally, the second follow-up was within 12–24 months after graduation (Figure 1).

Study assessmentsincluded healthendpoints,biomarkers, life stress exposure, behaviors and personal traits, social fac- tors, indicators of engagement and performance, and environ- mental exposures, which were conducted via clinical measures, biological samples, and self-reports as described in Supple- mental Digital Content 1, http://links.lww.com/NRES/A321. Clinical measures and biological samples were collected dur- ing laboratory visits. Serum and plasma were separated from blood samples by centrifugation within an hour of collection. Saliva wascollected usingthe passive drool technique (Salimetrics, 2015). Stool samples were collected by participants using two

454 Stress and Health in Nursing Students www.nursingresearchonline.com

Para-Pak vials: one containing 5 ml of RNAlater preservative and another containing 10 ml of glycerol solution and 2 ml of acid-washed glass beads. Participants placed a small, teaspoon-sized sample in each tube, stored the samples in their freezer in a designated package, and delivered the sample to the laboratory within 24 hours. Then, serum, plasma, saliva, and stool samples were stored at −80°C until analysis.

During each time point, participants also completed the list of self-assessments described in Supplemental Digital Content 1, http://links.lww.com/NRES/A321. Participants had 12 weeks, starting at the beginning of the academic semester, to complete these instruments. They were required to com- plete each assessment once enrolled in the cohort. The date and time of completion of each assessment were recorded.

Study Assessments

Health Endpoints and Biomarkers Indicators of metabolic health included systolic blood pressure (SBP), diastolic blood pressure(DBP),bodymassindex(BMI), andfastinglevelsoftri- glycerides, cholesterol (total, low-density lipoprotein [LDL], high-density lipoprotein [HDL]), glucose, and glycohemoglobin (A1c). Cardiovascular endpoints included carotid intima-media thickness (CIMT) measured via ultrasound (Stein et al., 2008), heart rate variability measured via electrocardiogram, and ret- inal blood vessel caliber measured via fundus imaging (De Boever, Louwies, Provost, Int Panis, & Nawrot, 2014). Retinal arterial narrowing has been shown to predict hypertension, and venular widening has been linked with systemic inflam- mation, endothelial dysfunction, and atherosclerosis (Adar et al., 2010; Louwies, Panis, Kicinski, De Boever, & Nawrot, 2013). Depressive severity was measured with the patient health questionnaire for depression (Patient Health Questionnaire-9). Symptoms of chemical sensitivity such as skin and eye irrita- tion, dizziness, pain, fatigue, stomachache, asthma and allergies

diagnosis, headache, and balance problems were assessed using a modified survey version of an existing environmental sensi- tivities survey (Andersson, Andersson, Bende, Millqvist, & Nordin, 2009; Nordin, Palmquist, & Claeson, 2013).

The health state of participants was assessed using the following definitions. Hypertension Stage 1 was defined as SBP between 130 and 139 mm Hg or DBP between 80 and 89mmHg (Wheltonet al., 2018). HypertensionStage 2 was de- finedas SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg (Whelton et al., 2018). Overweight was defined as BMI between 25 and 29 and obese asBMI ≥ 30 (Centers for Disease Control and Prevention [CDC], 2016). LDL ≥ 130 mg/dl was considered high, and HDL ≤ 40 mg/dl was considered low (Zwald, Akinbami, Fakhouri, & Fryar, 2017). Triglycerides of ≥150 mg/dl were considered elevated (Carroll, Kit, & Lacher, 2015). Prediabetes was defined as fasting glucose between 100 and 125 mg/dl or A1c between 5.7% and 6.4% (CDC, 2017). Diabetes was defined as fasting glucose >125 mg/dl or A1c > 6.4% (CDC, 2017). Mod- erate or worse depression severity was defined as a Patient Health Questionnaire-9 score ≥10 (Brody, Pratt, & Hughes, 2018).

Systemic inflammation was assessed via serum levels of C-reactive protein and the inflammatory cytokines interleu- kin (IL)-1β, IL-6, IL-8, and tumor necrosis factor-α (TNF-α). Serum levels of Fetuin A (FA) and Amyloid P (SAP) were also assessed as markers of cardiovascular disease risk. Serum samples were analyzed in duplicate wells using the Milliplex MultiAnalyte Profiling Human CVD Panel 3 premixed kit (EMD Millipore Corp., Billerica, MA) for C-reactive protein, FA and SAP, and the Human High-Sensitivity T-Cell premixed kit (EMD Millipore Corp., Billerica, MA) for IL-1β, IL-6, IL-8, and TNF-α. The plates were read on a Luminex 200 analyzer (Luminex Corporation, Austin, TX) running Milliplex Analyst Version 5.1 software (Vigene Tech, Inc., Carlisle, MA). Con- centrations for each biomarker were calculated in reference to a five-point best-fitting standard curve. Salivary cortisol levels

FIGURE 1. Schematic of the study design of Nurse Engagement and Wellness Study. Baseline assessments were conducted during their first 6 months in the bachelor of science in nursing program, the first follow-up was conducted 4 months prior to graduation, and the second follow-up was conducted 1 year after graduation. CM = clinical measures; BS = biological sampling; SA = self-assessments.

Nursing Research • November/December 2019 • Volume 68 • No. 6 Stress and Health in Nursing Students 455

were assessed using an enzyme immunoassay (Salimetrics, State College, PA).

Lifetime Stress Exposure Cumulative count and severity of all of the acute and chronic stressors that participants experi- enced across the lifespan were measured with the Stress and Adversity Inventory for Adults (Adult STRAIN; Slavich & Shields, 2018). The STRAIN is an online system for assessing stressors occurring across the life course that may exert a cu- mulative effect on biological processes that promote disease. The STRAIN adheres to commonly agreed-upon best practices by assessing the specific timing of stress exposure, distinguishing between different forms and types of stress, and accounting for both “objective” exposure (i.e., counts) as well as “subjective” experiences (i.e., perceived stress severity; Slavich & Shields, 2018). Because the STRAIN produces a set of indices for mul- tiple types of stressors (e.g., work, education, financial), it is ideally suited for the investigation of interactions between education-related stress and non-education-related stress among nursing students (see http://www.strainsetup.com).

Behaviors and Personal Traits The frequency of consump- tion by food type (i.e., vegetables, fruit, sugary drinks), type and duration of physicalactivity (e.g.,walking, swimming,run- ning), smoking frequency, frequency of drug and alcohol con- sumption, sleep duration, and sleep quality was obtained via self-reports. Personality type, stress coping style, forgiveness, empathy, and self-efficacy were measured with the psycho- metric instruments listed in Supplemental Digital Content 1, http://links.lww.com/NRES/A321. Emotional intelligence was assessed using the Mayer–Salovey–Caruso Emotional Intelligence Test and cognitive ability was assessed using the overclaiming technique (Paulhus & Harms, 2004).

Social Factors Socioeconomic status was assessed by mater- nal educational attainment, household income, poverty level, and median income of the census block group in which the participant resides. Childhood adversity was measured with the Adverse Childhood Experiences (ACE) questionnaire (Felitti et al., 1998). Social support (Mitchell et al., 2003) and attachment to parent and peers (Mattanah et al., 2011) were also assessed.

Engagement and Performance Endpoints Burnout was measured using the Maslach Burnout Inventory (Maslach & Jackson, 1981; Yavuz & Dogan, 2014). The student version of the generalsurvey (Maslach Burnout Inventory-GeneralSurvey for Students) was used at baseline and at first follow-up, and the human services survey was used at second follow-up. Aca- demic performance was assessed via course grades, grade point average, and nursing licensure examination (NCLEX) results. Voluntary attrition from nursing program was used as a measure of engagement. After graduation, clinical perfor- mance was tracked with the Six Dimension Scale of Nursing Performance (Schwirian, 1978).

Environmental Exposures Frequency and quantity of use of hazardous chemicals commonly encountered by nurses, including drugs (e.g., aerosolized antibiotics, antineoplastic drugs), sterilizers and disinfectants, anesthetic gases, and surgi- cal smoke, was assessed with a modified version of the Health and Safety Practices Survey of Healthcare Workers (Steege, Boiano, & Sweeney, 2014). The type and frequency of per- sonalcareproducts used in dailylife wereassessedwith a ques- tionnaire modified from Wu et al. (2010). Exposure to green spaces was assessed via satellite imagery (Browning & Lee, 2017) and Google Streetview images based on geocoded addresses (Li, Deal, Zhou, Slavenas, & Sullivan, 2018). Serum levels of cadmium, aluminum, iron, copper, and arsenic were measured via inductively coupled plasma mass spectrometry (CDC, 2012; Funk, Pleil, Sauter, McDade, & Holl, 2015; Harkema et al., 2009).

Data Analysis

Descriptive statistics (e.g., means, standard deviations, and percentages) were used to describe the prevalence of com- mon health conditions in the cohort, which in turn were com- pared against the national prevalence for adults between 20 and 39 years of age using the most recent data from the National Health and Nutrition Survey (CDC, 2018), unless oth- erwise specified. Biomarker (e.g., IL-1β, IL-6, IL-8, TNFα, FA, and SAP) and metal levels were transformed with a logarith- mic function (base 10) and subsequently standardized. Odds ratios (ORs) were used as measures of associations between the life stress variables and unhealthy states (e.g., hypertension, obesity, depression). For life stress variables, tertiles were cal- culated and ORs were calculated for top versus bottom tertiles via binomial logistic regression. ORs were adjusted for age and gender based on evidence showing that these variables can affect associations between stress and health (Appelman, van Rijn, Ten Haaf, Boersma, & Peters, 2015; Mazure & Swendsen, 2016; Slavich & Irwin, 2014; Tamres, Janicki, & Helgeson, 2002). Analysis of covariance were used to character- ize the variance of biomarkers (e.g., cytokines, A1c) across stress, behavioral, and environmental variables while control- ling for age and gender.

RESULTS

Demographic and Behavioral Characteristics

At baseline, the cohort included 436 participants, 20% of whom were men, with an average age of 25.2 years old (SD = 2.3). Most participants self-identified as White (93%) and Hispanic (90%), and 20% were born outside the United States. Also, most participants (98%) were full-time students, just a few (4%) had a full-time job, 29% had a part-time job, 20% were living below the poverty line, and 20% were married. A small percentage of participants were current smokers (5%) or drank alcohol more than once a week (7%). Most participants ate fruits

456 Stress and Health in Nursing Students www.nursingresearchonline.com

(79%) and vegetables (83%) at least twice a week. Although 86% of participants slept at least 7 hours on a typical night, 61% did not rate their sleep quality as good. Twenty-one per- cent of participants had an ACE score equal or greater than 4, which is 7 percentage points higher than in the general U.S. population (CDC, 2015). Descriptive statistics are provided in Supplemental Digital Content 2, http://links.lww.com/ NRES/A322.

Lifetime Stress Exposure

Participants were exposed to an average of 14.8 stressors across the lifespan (SD = 10.7; range, 1–60; possible range, 0–166) as assessed by the Adult STRAIN, with an average over- all severity score of 35.3 (SD = 26.0; range, 0–115; possible range, 0–265). When compared to students from the same university but enrolled in other majors (n = 1,186), nursing students experienced fewer total stressors over the life course, F(1, 1807) = 102.4, p < .001, with relatively lower cumulative lifetime severity, F(1, 1807) = 82.5, p < .001 (Figure 2). Students from other majors reported an average lifetime stressor count of 20.0 (SD = 12.3; range, 1–111) and an average overall cumulative lifetime stressor severity of 49.9 (SD = 30.3; range, 0–245). The variation of stressor count and severity scores across the primary life domains and core social–psychological characteristics assessed by the Adult STRAIN was similar and consistent with prior results (Slavich & Shields, 2018); there- fore, we focused the main results on stressor counts.

Women experienced an average of 15.3 stressors across the lifespan (SD = 10.2) as compared to 13.2 experienced by men (SD = 12.1, p = .15). The life stressors most commonly endorsed were from “Other Relationships” (23% by women, 25% by men) and “Marital/Partner” sources (21% by women, 18% by men; Figure 2A). With respect to the core social– psychological characteristics, the stressors most commonly experienced were “Interpersonal Loss” (27% by women, 30% by men) and “Role Change/Disruption” (23% by women, 21% by men; Figure 2B).

Across the lifespan, Hispanic participants experienced an average of 14.5 stressors (SD = 10.2) as compared to 17.3 (SD = 13.4) by non-Hispanics (p = .13). Participants with a full-time job experienced an average of 19.2 stressors across the lifespan (SD = 11.1) as compared to 15.5 (SD = 10.1) for those with a part-time job (p = .22) and 14.2 (SD = 10.9) for those without a job (p = .11). Participants younger than 25 years old experienced an average of 12.1 stressors across the lifespan (SD = 8.8), whereas those 25 years of age or older experienced an average of 19.6 stressors (SD = 12.0), with the difference between these groups being significant, F(1, 326) = 42.53, p < .001. Participants with an ACE score of 0 reported a significantly lower lifetime stress exposure (mean = 8.78, SD = 5.55) than participants with an ACE score of ≥4 (M = 25.1, SD = 12.4), F(1, 158) = 128.1, p < .001. A sig- nificant difference persisted when comparing the average life- time stressor count of participants with ACE scores of ≥4 and

FIGURE 2. Comparison of lifetime stressor exposure by stressor category for nursing and nonnursing male and female students. (A) Nursing students ex- perienced a lower average total exposure to stressors across the lifetime than nonnursing students, F(1, 1511) = 49.63, p < .001. With respect to the life domains, female nursing students experienced more marital/partner stressors, F(1, 326) = 4.21, p = .041, and reproduction stressors, F(1, 326) = 8.35, p = .004, than male nursing students. (B) With respect to stress exposure across core social–psychological characteristics, there were no significant differ- ences between female and male nursing students. Color image is available only in online version.

Nursing Research • November/December 2019 • Volume 68 • No. 6 Stress and Health in Nursing Students 457

http://links.lww.com/NRES/A322

those with ACE scores of <4 (M = 12.3, SD = 8.54), F(1, 325) = 94.72, p < .001.

Health Conditions at Baseline

The prevalence of Stage 2 hypertension in the NEWS cohort (9%) was similar to the national prevalence (8%). Overweight (24%) and obesity (20%) in the cohort was lower than the cor- responding national prevalence (30% and 38%). The preva- lence of high LDL levels (12%) and low HDL levels in the cohort (20%) was close to their respective national prevalence (12% and 21%, respectively). The prevalence of elevated tri- glycerides (34%) in the cohort was higher than the national prevalence (20%) among individuals between 20 and 39 years butwasclosertothesameprevalenceamongHispanicwomen (28%) older than 20 years. Prediabetes prevalence (13%) was less than the national average (24%) among individuals be- tween 18 and 44 years of age (CDC, 2017). The prevalence of moderate or worse depression severity (23%) was higher than the national prevalence (8%).

Compared to men, women in the NEWS cohort had lower oddsofhaving hypertension(Stage 1 or2), highLDL,low HDL, beingoverweight,orhaving diabetes(forORs,seeFigure3;for summary statistics, see Supplemental Digital Content 3, http:// links.lww.com/NRES/A323). Conversely, women had higher odds of being obese or having moderate or worst depression severity than men. Hispanics had lower odds of being diabetic and of suicidal ideation than non-Hispanics.

Several patterns of associations between participants’ life stress exposure and health conditions were observed (for ORs, see Figure 3; for summary statistics, see Supplemental Digital Content 3, http://links.lww.com/NRES/A323). Partici- pants with high early life stress exposure (stressor counts in the top tertile) or with ACE scores of ≥4 had lower odds of having elevated triglycerides as compared to participants with low early life stress exposure (stressor counts in the bottom tertile) or to participants with an ACE score equal to zero, respectively. Compared to low lifetime exposure to marital/ partner stressors, high lifetime exposure to marital/partner stressors was associated with greater odds of both Stage 1 and Stage 2 hypertension (see Figure 3). Similarly, elevated tri- glycerides were positively associated with lifetime exposure to education, work, and reproductive stressors. Being over- weight was positively associated with stress exposure during adulthood, as well as with lifetime exposure to education, work, marital/partner, and legal/crime stressors. Being obese was positively associated with lifetime exposure to reproduc- tive, legal/crime, and possessions stressors (see Figure 3). Moderate or worst depression was positively associated with total lifetime stress exposure, early life stress exposure and ACE scores of ≥4, and lifetime stressors involving hous- ing, treatment/health, other relationships, entrapment, and role change/disruption.

Biomarker Levels

The analysis of covariance also revealed several significant as- sociations between participants’ lifetime stress exposure and biomarker levels (for detailed results, see Supplemental Digital Content 4, http://links.lww.com/NRES/A324 and Supplemen- tal Digital Content 5, http://links.lww.com/NRES/A325). For instance, IL-8 was positively associated with stress exposure across the lifespan, F(1, 208) = 4.37, p = .038, as well as with lifetime exposure to financial, F(1, 208) = 5.16, p = .024, and life-threatening situation stressors, F(1, 209) = 4.09, p = .045. IL-6 was negatively associated with lifetime exposure to hous- ing, F(1, 209) = 3.57, p = .060; education, F(1, 207) = 3.37, p = .068; and reproductive stressors, F(1, 206) = 3.06, p = .082. TNF-α was negatively associated with lifetime expo- sure to housing, F(1, 209) = 3.61, p = .059; education, F(1, 207) = 4.66, p = .032; work, F(1, 208) = 3.11, p = .079; and pos- sessions stressors, F(1,206) = 3.69, p = .056. Lifetime exposure to possession stressors was also negatively associated with IL-8 F(1, 206) = 2.74, p = .099.

CIMT was positively associated with lifetime exposure to stressors from multiple sources, including housing, F(1, 202) = 9.97, p = .002; education, F(1, 198) = 9.53, p = .002; work, F(1, 207) = 10.43, p = .001; reproductive, F(1, 198) = 18.42, p < .001; financial, F(1, 206) = 7.83, p = .006; legal, F(1, 198) = 13.83, p < .001; and possessions, F(1, 197) = 11.76, p < .001. RBC was negatively associated with lifetime stress exposure, F(1, 34) = 3.53, p = .069, stress expo- sure occurring during both early life, F(1, 31) = 4.36, p = .045, and adulthood, F(1, 34) = 5.02, p = .032, as well as with life- time exposure to housing, F(1, 34) = 5.94, p = .023; marital/ partner, F(1, 34) = 7.87, p = .008; and financial stressors, F(1, 34) = 8.23, p = .012. Lifetime exposure to stressors involv- ing treatment/health was positively associated with lympho- cyte, F(1, 22) = 5.57, p = .028, and basophil counts, F(1, 22) = 7.08, p = .014. Finally, ACE score was negatively associ- ated with WBC, F(1, 33) = 4.98, p = .033, and RBC, F(1, 33) = 4.06, p = .052, as well as with neutrophil, F(1, 33) = 4.05, p = .052, and monocyte counts, F(1, 33) = 6.51, p = .016.

DISCUSSION

These results represent one of the most comprehensive sum- maries of the lives and health of nursing students to date. Prev- alence of most health conditions measured at baseline in the NEWS cohort was comparable—and sometimes better—than the U.S. population. The prevalence of moderate-to-severe de- pression in the cohort was comparable to the prevalence (26%) among North American nursing students estimated via meta-analysis (Tung, Lo, Ho, & Tam, 2018) and might be par- tially explained by the fact that our cohort is predominately young, Hispanic, and female. Evidence shows that females (World Health Organization, 2012), Hispanics (Shattell, Smith, Quinlan-Colwell, & Villalba, 2008), and college students (Buchanan, 2012) are particularly susceptible to depression.

458 Stress and Health in Nursing Students www.nursingresearchonline.com

FIGURE 3. Odd ratios and 95% confidence intervals for selected health conditions by life stressor type and selected factors, adjusting for age and gender. Hypertension: Stage 1, systolic blood pressure 130–139 mm Hg or diastolic blood pressure 80–89 mm Hg; Stage 2, systolic blood pressure � 140 mm Hg or diastolic blood pressure � 90 mm Hg. High low-density lipoproteins levels � 130 mg/dl, low high-density lipoproteins levels � 40 mg/dl, elevated triglyc- erides � 150 mg/dl. Overweight, body mass index 25–29; obesity, body mass index � 30. Prediabetes, fasting glucose 100–125 mg/dl or glycated hemo- globin (A1c) 5.7%–6.4%; diabetes fasting glucose � 125 mg/dl or A1c � 6.4%. Depression, Patient Health Questionnaire-9 score � 10, which represents moderate or worse severity. TT = top tertile; BT = bottom tertile. †p < .10, *p < .05, **p < .01, ***p < .001. ° = unadjusted.

Nursing Research • November/December 2019 • Volume 68 • No. 6 Stress and Health in Nursing Students 459

NEWS participants also reported fewer lifetime stressors than students from all other majors at the same university and ap- proximately half as many lifetime stressors—with approxi- mately half the total severity—as was recently reported in a nationwide assessment of adults who completed the STRAIN (Slavich & Shields, 2018).

AlthoughNEWS participants generallyexhibitedrelatively good health, there was substantial variability in health risk fac- tors, thus supporting the need to assess factors that might pre- dict disease risk on an individual basis. For example, elevated exposure to lifetime stressors was associated with greater risk of hypertension, diabetes, and depression. More education- related stressors, in turn, were associated with elevated tri- glycerides, BMI, and CIMT, but also with lower inflammation (e.g., TNF-α).

The relevance of investigating the effects of life stress and other factors on health trends in young adults—especially among high-stress exposure groups such as nursing students— is supported by studies showing that health during early life and young adulthood affects lifespan risk for health problems. For example, a case–control study found that depressive dis- orders often emerge in childhood and continue through the adult life (Harrington, Fudge, Rutter, Pickles, & Hill, 1990). Likewise, a longitudinal study revealed that cardiovascular risk factors such as blood pressure and cholesterol levels in early adulthood predicted subsequent risk of cardiovascular disease in older adulthood (Pletcher, Vittinghoff, Thanataveerat, Bibbins-Domingo, & Moran, 2016).

Among nursing students specifically, it is unclear how health conditions that are present duringnursing school affect, or are affected by, the transition into the work environment and what role stress plays in structuring these effects. It is im- portant to determine the extent to which health risks, suscep- tibilities, and psychosocial assets persist into the early stages of the nursing profession. Moreover, if these factors persist into the work environment, it will be important to understand how these factors interact and how they moderate or mediate the effects of lifetime stress exposure on health.

Strengths and Limitations

Several strengths and limitations of this study are noteworthy. For example, although weonlyreportbaseline data in thepres- ent article, the NEWS cohort provides a prospective cohort study design, which permits the examination of how early life stressors—as well as behavioral, social, and environmental factors—affect young nurses’ susceptibility and resilience to stress in the workforce. Also, because most students at Univer- sity of Texas at El Paso progress through the BSN program as a cohort and hence experience the same coursework and simi- lar clinical experiences, the NEWS supports the assessment of students’ sensitivity to education-related stress and modify- ing factors as they progress through the program. In contrast, an important limitation to the NEWS is the temporal

discordance of some assessments. We plan to account for mea- sures that are not perfectly aligned in time by relying on assess- ment dates, course, and clinical training schedules and by asking participants to report on historic data in questionnaires (e.g., “in the last 2 weeks…”). Still, we might not be able to overcome the fact that some measures are collected at differ- ent times in some important analyses. In these cases, the inter- pretation of results will account for this discordance. Another limitation of the NEWS is that some analyses might fail to prop- erly reject the null hypothesis of no gender effect, given the limited number of males in the cohort.

Importantly, the observations from this study were drawn from a cohort that is predominately Hispanic (90%), with a sub- stantial proportion being foreign-born (20%). Baseline measures and observed associations may thus not apply to the general population of nursing students across the United States, which in 2014 included 28% racial-ethnic minorities, 8% Hispanics, and 4%foreign-born(NationalLeague for Nursing, 2014).Forinstance, minority populations (American Psychological Association, 2017) as well as foreign-born nursing students (Vardaman & Mastel- Smith, 2016) experience higher levels of stress as compared to Whites and U.S.-born students, respectively. Evidence also shows that Hispanics are at higher risk than non-Hispanics of developing health problems (e.g., diabetes) that are typi- cally associated with stress exposure (CDC, 2004).

Despite these shortcomings, the results of this study are valuable as they represent one of the most comprehensive studies on Hispanics and foreign-born nursing students to date. Studying the stress–health relationship in this group of future nurses is timely and relevant, as both the U.S. population and the healthcare force in the United States are rapidly becoming more diverse (Patel, Ly, Hicks, & Jena, 2018).

CONCLUSION

In conclusion, as the U.S. population ages and the incidence of chronic illness continues to rise, ensuring the availability of a competent nursing force to deal with these challenges will be a central challenge for healthcare systems. Meeting this de- mand will be difficult if the present shortage of nurses persists. Indeed, workforce problems could get worse if work-related stressors increase and affect the prevalence of major health problems innew nurses. Likewise,it is urgent that we enhance the learning and working environments of registered nurses in ways that increase their capacity to attain and conserve physi- cal and psychological well-being. These efforts will help im- prove the long-term health and well-being of nurses, as well as reduce turnover and attrition in hospitals.

Given the unique demographic composition of its cohort, we believe that the NEWS will result in important contribu- tions to these efforts by identifying factors that could be targeted by schools of nursing and healthcare institutions to minimize the effect of stress on young Hispanic male and female nurses. Indeed, understanding how young Hispanic

460 Stress and Health in Nursing Students www.nursingresearchonline.com

nurses respond to, and are affected by, life stress is timely and relevant. Institutions like the Health Resources and Services Administration’s Division of Nursing and the National Institute of Nursing Research are striving to diversify the nursing profession to respond to healthcare needs and disparity-associated chal- lenges of a diversifying U.S. population, and additional research is warranted to help ensure the sustained good physical health, psychological wellness, and productivity of this workforce.

Accepted for publication February 3, 2019.

This work was supported by a JPB Environmental Health Fellowship award granted to Hector A. Olvera Alvarez by the JPB Foundation and managed by the Harvard T. H. Chan School of Public Health; by Grant 5G12MD007592 from the National Institutes on Minority Health and Health Disparities, a component of the National Institutes of Health, to Hector Olvera Alvarez; by the Orville Edward Egbert, MD Chair endowment to Leslie Robbins; and by the Hoffman Program on Chemicals and Health at the Harvard T. H. Chan School of Public Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors would like to thank the nursing students who participated in this study for their support. They also thank the nursing faculty and the staff of the Biobehavioral Research Laboratory at the School of Nursing at the University of Texas at El Paso for supporting the recruitment and data collection efforts of this study.

The authors have no conflicts of interest to report.

Corresponding author: Hector A. Olvera Alvarez, PhD, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968 (e-mail: [email protected]).

Ethical Conduct of Research: The institutional review boards at University of Texas at El Paso (857149-1) and Harvard University (16-0080) approved this study.

Clinical Trial Registration: None applicable.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.nursingresearchonline.com).

REFERENCES

Adar, S. D., Klein, R., Klein, B. E., Szpiro, A. A., Cotch, M. F., Wong, T. Y., … Kaufman, J. D. (2010). Air pollution and the microvasculature: A cross-sectional assessment of in vivo retinal images in the population-based multi-ethnic study of atherosclerosis (MESA). PLoS Medicine, 7, e1000372. doi:10.1371/journal.pmed.1000372

Aldwin, C. M., Spiro, A.3rd, Levenson, M. R., & Cupertino, A. P. (2001). Longitudinal findings from the Normative Aging Study: III. Personality, individual health trajectories, and mortality. Psy- chology and Aging, 16, 450–465. doi:10.1037/0882-7974.16.3.450

American Psychological Association (2017). Stress and health dispar- ities: Contexts, mechanisms, and interventions among racial/

ethnic minority and low-socioeconomic status populations. Washington, DC: Author.

Andersson, M. J., Andersson, L., Bende, M., Millqvist, E., & Nordin, S. (2009). The idiopathic environmental intolerance symptom inventory: Development, evaluation, and application. Journal of Occupational and Environmental Medicine, 51, 838–847. doi:10.1097/JOM.0b013e3181a7f021

Appelman, Y., van Rijn, B. B., Ten Haaf, M. E., Boersma, E., & Peters, S. A. (2015). Sex differences in cardiovascular risk factors and dis- ease prevention. Atherosclerosis, 241, 211–218. doi:10.1016/j. atherosclerosis.2015.01.027

Bell, I. R., Baldwin, C. M., Russek, L. G., Schwartz, G. E., & Hardin, E. E. (1998). Early life stress, negative paternal relationships, and chemical intolerance in middle-aged women: Support for a neural sensitization model. Journal of Women’s Health, 7, 1135–1147. doi:doi.org/10.1089/jwh.1998.7.1135

Black, D. W., Okiishi, C., Gabel, J., & Schlosser, S. (1999). Psychiatric illness in the first-degree relatives of persons reporting multi- ple chemical sensitivities. Toxicology and Industrial Health, 15(3–4), 410–414. doi:10.1177/074823379901500315

Blythe, J., Baumann, A., Zeytinoglu, I. U., Denton, M., Akhtar-Danesh, N., Davies, S., & Kolotylo, C. (2008). Nursing generations in the con- temporary workplace. Public Personnel Management, 37, 137–159. doi:10.1177/009102600803700201

Brody, D. J., Pratt, L. A., & Hughes, J. P. (2018). Prevalence of depres- sion among adults aged 20 and over: United States, 2013–2016. NCHS Data Brief, 303, 1–8.

Browning, M., & Lee, K. (2017). Within what distance does “green- ness” best predict physical health? A systematic review of articles with GIS buffer analyses across the lifespan. International Journal of Environmental Resesearch and Public Health, 14. doi:10.3390/ ijerph14070675

Buchanan, J. L. (2012). Prevention of depression in the college stu- dent population: A review of the literature. Archives of Psychiatric Nursing, 26, 21–42. doi:10.1016/j.apnu.2011.03.003

Carroll, M., Kit, B., & Lacher, D. (2015). Trends in elevated triglyceride in adults: United States, 2001–2012. NCHS Data Brief, 198, 198.

Cecil, J., McHale, C., Hart, J., & Laidlaw, A. (2014). Behaviour and burnout in medical students. Medical Education Online, 19, 25209. doi:10.3402/meo.v19.25209

Centers for Disease Control and Prevention (2004). Prevalence of diabetes among Hispanics—Selected areas, 1998–2002. MMWR Morbidity and Mortality Weekly Report, 53, 941–944.

Centers for Disease Control and Prevention (2012). Laboratory pro- cedure manual: Cadmium, lead, manganese, mercury, and sele-

nium. Retrieved from https://www.cdc.gov/Nchs/Data/Nhanes/ Nhanes_13_14/PbCd_H_MET.pdf

Centers for Disease Control and Prevention (2015). Adverse Child- hood Experiences (ACEs). Retrieved from https://www.cdc. gov/violenceprevention/acestudy/index.html

Centers for Disease Control and Prevention (2016). Defining adult overweight and obesity. Retrieved from https://www.cdc.gov/ obesity/adult/defining.html

Centers for Disease Control and Prevention (2017). National diabetes statistics report. Retrieved from https://www.cdc.gov/diabetes/ data/statistics/statistics-report.html

Centers for Disease Control and Prevention (2018). NHANES questionanires, datasets, and related documentation. Retrieved from https://wwwn.cdc.gov/nchs/nhanes/default.aspx

De Boever, P., Louwies, T., Provost, E., Int Panis, L., & Nawrot, T. S. (2014). Fundus photography as a convenient tool to study micro- vascular responses to cardiovascular disease risk factors in epide- miological studies. Journal of Visualized Experiments, 92, e51904. doi:10.3791/51904

Elfering, A., Semmer, N. K., & Grebner, S. (2006). Work stress and patient safety: Observer-rated work stressors as predictors of char- acteristics of safety-related events reported by young nurses. Ergo- nomics, 49(5–6), 457–469. doi:10.1080/00140130600568451

Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., & Marks, J. S. (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14, 245–258. doi:10.1016/S0749-3797(98)00017-8

Nursing Research • November/December 2019 • Volume 68 • No. 6 Stress and Health in Nursing Students 461

Fink, R., Krugman, M., Casey, K., & Goode, C. (2008). The graduate nurse experience: Qualitative residency program outcomes. Jour- nal of Nursing Administration, 38(7–8), 341–348. doi:10.1097/ 01.NNA.0000323943.82016.48

Funk, W. E., Pleil, J. D., Sauter, D. J., McDade, T., & Holl, J. L. (2015). Use of dried blood spots for estimating children’s exposures to heavy metals in epidemiological research. Journal of Environmental & Analytical Toxicology, S7. doi.org/10.4172/2161-0525.S7-002

Gleeson, M., Bishop, N. C., Stensel, D. J., Lindley, M. R., Mastana, S. S., & Nimmo, M. A. (2011). The anti-inflammatory effects of exercise: Mechanisms and implications for the prevention and treatment of disease. Nature Reviews Immunology, 11, 607–615. doi:10.1038/ nri3041

Harkema, J. R., Wagner, J. G., Kaminski, N. E., Morishita, M., Keeler, G. J., McDonald, J. D., Barrett, E. G. & HEI Health Review Commit- tee (2009). Effects of concentrated ambient particles and diesel engine exhaust on allergic airway disease in Brown Norway rats. Research Report (Health Effects Institute), 145, 5–55.

Harrington, R., Fudge, H., Rutter, M., Pickles, A., & Hill, J. (1990). Adult outcomes of childhood and adolescent depression. I. Psy- chiatric status. Archives of General Psychiatry, 47, 465–473. doi:10.1001/archpsyc.1990.01810170065010

Hayes, L. J., O'Brien-Pallas, L., Duffield, C., Shamian, J., Buchan, J., Hughes, F., … North, N. (2012). Nurse turnover: A literature re- view: An update. International Journal of Nursing Studies, 49, 887–905. doi:doi.org/10.1016/j.ijnurstu.2011.10.001

Judge, T. A., & Ilies, R. (2002). Relationship of personality to perfor- mance motivation: A meta-analytic review. Journal of Applied Psychology, 87, 797–807. doi:10.1037/0021-9010.87.4.797

Karasek, R., & Theorell, T. (1990). Healthy work: Stress, produc- tivity, and the reconstruction of working life. New York, NY: Basic Books.

Karimi, A., Adel-Mehraban, M., & Moeini, M. (2018). Occupational stressors in nurses and nursing adverse events. Iranian Journal of Nursing and Midwifery Research, 23, 230–234. doi:10.4103/ ijnmr.IJNMR_253_15

Kovner, C. T., Brewer, C. S., Fatehi, F., & Jun, J. (2014). What does nurse turnover rate mean and what is the rate? Policy, Politics, & Nursing Practice, 15(3–4), 64–71. doi:10.1177/1527154414547953

Kovner, C. T., & Djukic, M. (2009). The nursing career process from ap- plication through the first 2 years of employment. Journal of Pro- fessional Nursing, 25, 197–203. doi:10.1016/j.profnurs.2009.05.002

Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of Gen- eral Internal Medicine, 16, 606–613. doi:10.1046/j.1525-1497. 2001.016009606.x

Li, D., Deal, B., Zhou, X., Slavenas, M., & Sullivan, W. C. (2018). Moving beyond the neighborhood: Daily exposure to nature and adolescents’ mood. Landscape and Urban Planning, 173, 33–43. doi:10.1016/j.landurbplan.2018.01.009

Louwies, T., Panis, L. I., Kicinski, M., De Boever, P., & Nawrot, T. S. (2013). Retinal microvascular responses to short-term changes in particulate air pollution in healthy adults. Environmental Health Perspectives, 121, 1011–1016. doi:10.1289/ehp.1205721

Maslach, C., & Jackson, S. E. (1981). The measurement of experi- enced burnout. Journal of Occupational Behaviour, 2, 9–113. https://doi.org/10.1002/job.4030020205

Mattanah, J. F., Lopez, F. G., & Govern, J. M. (2011). The contributions of parental attachment bonds to college student development and adjustment: A meta-analytic review. Journal of Counseling Psy- chology, 58, 565–596. doi:10.1037/a0024635

Mazure, C. M., & Swendsen, J. (2016). Sex differences in Alzheimer's disease and other dementias. The Lancet Neurology, 15, 451–452. doi:10.1016/S1474-4422(16)00067-3

McEwen, B. S. (1998). Stress, adaptation, and disease: Allostasis and allostatic load. Annals of the New York Academy of Sciences, 840, 33–44. doi.org/10.1111/j.1749-6632.1998.tb09546.x

McNeely, E. (2005). The consequences of job stress for nurses' health: Time for a check-up. Nursing Outlook, 53, 291–299. doi:10.1016/ j.outlook.2005.10.001

Minihane, A. M., Vinoy, S., Russell, W. R., Baka, A., Roche, H. M., Tuohy, K. M., … Calder, P. C. (2015). Low-grade inflammation, diet composition and health: Current research evidence and its translation. British Journal of Nutrition, 114, 999–1012. doi:10.1017/S0007114515002093

Mitchell, P. H., Powell, L., Blumenthal, J., Norten, J., Ironson, G., Pitula, C. R., … Berkman, L. F. (2003). A short social support mea- sure for patients recovering from myocardial infarction: The ENRICHD Social Support Inventory. Journal of Cardiopulmonary Rehabilitation and Prevention, 23, 398–403. doi:10.1097/ 00008483-200311000-00001

Moreira, D. P., & Furegato, A. R. (2013). Stress and depression among students of the last semester in two nursing courses. Revista Latino-Americana de Enfermagem, 21, 155–162. doi:10.1590/ S0104-11692013000700020

Moustaka, E., & Constantinidis, T. C. (2010). Sources and effects of work-related stress in nursing. Health Science Journal, 4, 210–216.

National League for Nursing (2014). Annual survey of schools of nursing. Academic Year, 2013–2014. Retrieved from http://www. nln.org/newsroom/nursing-education-statistics/annual-survey-of- schools-of-nursing-academic-year-2013-2014

Newman, K., Maylor, U., & Chansarkar, B. (2001). The nurse reten- tion, quality of care and patient satisfaction chain. International Journal of Health Care Quality Assurance, 14(2–3), 57–68. doi:10.1108/09526860110386500

Nordin, S., Palmquist, E., & Claeson, A. S. (2013). The Environmental Symptom-Attribution Scale: Metric properties and normative data. Journal of Environmental Psychology, 36, 9–17. doi:10.1016/j. jenvp.2013.06.006

Nusslock, R., & Miller, G. E. (2016). Early-life adversity and physical and emotional health across the lifespan: A neuroimmune net- work hypothesis. Biological Psychiatry, 80, 23–32. doi:10.1016/j. biopsych.2015.05.017

Olvera Alvarez, H. A., Kubzansky, L. D., Campen, M. J., & Slavich, G. M. (2018). Early life stress, air pollution, inflammation, and disease: An integrative review and immunologic model of social- environmental adversity and lifespan health. Neuroscience and Biobehavioral Reviews, 92, 226–242. doi:10.1016/j.neubiorev. 2018.06.002

Oyeleye, O., Hanson, P., O'Connor, N., & Dunn, D. (2013). Relation- ship of workplace incivility, stress, and burnout on nurses' turnover intentions and psychological empowerment. Journal of Nursing Administration, 43, 536–542. doi:10.1097/NNA.0b013e3182a3e8c9

Patel, Y. M., Ly, D. P., Hicks, T., & Jena, A. B. (2018). Proportion of non- US-born and noncitizen health care professionals in the United States in 2016. JAMA, 320, 2265–2267. doi:10.1001/jama. 2018.14270

Paulhus, D. L., & Harms, P. D. (2004). Measuring cognitive ability with the overclaiming technique. Intelligence, 32, 297–314. doi:10.1016/j. intell.2004.02.001

Pletcher, M. J., Vittinghoff, E., Thanataveerat, A., Bibbins-Domingo, K., & Moran, A. E. (2016). Young adult exposure to cardiovascular risk factors and risk of events later in life: The Framingham Off- spring Study. PLOS ONE, 11, e0154288. doi:10.1371/journal. pone.0154288

Pulido-Martos, M., Augusto-Landa, J. M., & Lopez-Zafra, E. (2012). Sources of stress in nursing students: A systematic review of quantita- tive studies. International Nursing Review, 59, 15–25. doi:10.1111/ j.1466-7657.2011.00939.x

462 Stress and Health in Nursing Students www.nursingresearchonline.com

Roberts, R. K., & Grubb, P. L. (2014). The consequences of nursing stress and need for integrated solutions. Rehabilitation Nursing, 39, 62–69. doi:10.1002/rnj.97

Rudman, A., & Gustavsson, J. P. (2012). Burnout during nursing educa- tion predicts lower occupational preparedness and future clinical performance: A longitudinal study. International Journal of Nurs- ing Studies, 49, 988–1001. doi:10.1016/j.ijnurstu.2012.03.010

Rudman, A., Omne-Ponten, M., Wallin, L., & Gustavsson, P. J. (2010). Monitoring the newly qualified nurses in Sweden: The Longitudinal Analysis of Nursing Education (LANE) study. Human Resources for Health, 8, 10. doi:10.1186/1478-4491-8-10

Salimetrics (2015). Saliva collection handbook. Retrieved from https://www.salimetrics.com/saliva-collection-handbook/

Schwirian, P. M. (1978). Evaluating the performance of nurses: A multidimensional approach. Nursing Research, 27, 347–351. doi:10.1097/00006199-197811000-00004

Shader, K., Broome, M. E., Broome, C. D., West, M. E., & Nash, M. (2001). Factors influencing satisfaction and anticipated turnover for nurses in an academic medical center. Journal of Nursing Ad- ministration, 31, 210–216.

Shattell, M. M., Smith, K. M., Quinlan-Colwell, A., & Villalba, J. A. (2008). Factors contributing to depression in latinas of mexican origin residing in the United States: Implications for nurses. Journal of the American Psychiatric Nurses Association, 14, 193–204. doi:10.1177/1078390308319034

Slavich, G. M., & Irwin, M. R. (2014). From stress to inflammation and major depressive disorder: A social signal transduction theory of depression. Psychological Bulletin, 140, 774–815. doi:10.1037/ a0035302

Slavich, G. M., & Shields, G. S. (2018). Assessing lifetime stress expo- sure using the Stress and Adversity Inventory for Adults (Adult STRAIN): An overview and initial validation. Psychosomatic Med- icine, 80, 17–27. doi:10.1097/PSY.0000000000000534

Squillace, M. R., Bercovitz, A., Rosenoff, E., & Remsburg, R. (2008). An Exploratory Study of Certified Nursing Assistants’ Intent to Leave. Retrieved from https://aspe.hhs.gov/system/files/pdf/ 75566/intent.pdf

Steege, A. L., Boiano, J. M., & Sweeney, M. H. (2014). NIOSH health and safety practices survey of healthcare workers: Training and awareness of employer safety procedures. American Journal of Industrial Medicine, 57, 640–652. doi:10.1002/ajim.22305

Stein, J. H., Korcarz, C. E., Hurst, R. T., Lonn, E., Kendall, C. B., Mohler, E. R., … Post, W. S. (2008). Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular

disease risk: A consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force endorsed by the Society for Vascular Medicine. Journal of the American Society of Echocardiography, 21, 93–111. doi:10.1016/j. echo.2007.11.011

Tamres, L. K., Janicki, D., & Helgeson, V. S. (2002). Sex differences in coping behavior: A meta-analytic review and an examination of relative coping. Personality and Social Psychology Review, 6, 2–30. doi:10.1207/S15327957pspr0601_1

Thoits, P. A. (2011). Mechanisms linking social ties and support to physical and mental health. Journal of Health and Social Behavior, 52, 145–161. doi:10.1177/0022146510395592

Tung, Y. J., Lo, K. K. H., Ho, R. C. M., & Tam, W. S. W. (2018). Prev- alence of depression among nursing students: A systematic review and meta-analysis. Nurse Education Today, 63, 119–129. doi:10.1016/ j.nedt.2018.01.009

Turner, K., & McCarthy, V. L. (2017). Stress and anxiety among nurs- ing students: A review of intervention strategies in literature be- tween 2009 and 2015. Nurse Education in Practice, 22, 21–29. doi:10.1016/j.nepr.2016.11.002

Vardaman, S. A., & Mastel-Smith, B. (2016). The transitions of interna- tional nursing students. Teaching and Learning in Nursing, 11, 34–43. doi:10.1016/j.teln.2015.12.008

Whelton, P. K., Carey, R. M., Aronow, W. S., Casey, D. E.Jr., Collins, K. J., Dennison Himmelfarb, C., … Wright, J. T.Jr. (2018). 2017 ACC/AHA/ AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and managementof high blood pressure in adults: Executive summary: A report of the American Col- lege of Cardiology/American Heart Association Task Force on clinical practice guidelines. Journal of the American College of Cardiology, 71, 2199–2269. doi:10.1016/j.jacc.2017.11.005

World Health Organization (2012). Depression. Retrieved from http://www.who.int/mediacentre/factsheets/fs369/en/

Wu, X. M., Bennett, D. H., Ritz, B., Cassady, D. L., Lee, K., & Hertz- Picciotto, I. (2010). Usage pattern of personal care products in California households. Food and Chemical Toxicology, 48, 3109–3119. doi:10.1016/j.fct.2010.08.004

Yavuz, G., & Dogan, N. (2014). Maslach Burnout Inventory-Student Survey (MBI-SS): A validity study. Procedia—Social and Behav- ioral Sciences, 116, 2453–2457. doi:10.1016/j.sbspro.2014.01.590

Zwald, M. L., Akinbami, L. J., Fakhouri, T. H., & Fryar, C. D. (2017). Prevalence of low high-density lipoprotein cholesterol among adults, by physical activity: United States, 2011-2014. NCHS Data Brief, 276, 1–8.

Read and Send Letters to the Editor

Nursing Research now publishes Letters to the Editor in the print journal. To submit a Letter to the Editor you should login to Editorial Manager here: http://www.editorialmanager.com/nres/default.aspx and choose article type ‘Letter to the Editor.’ Authors of articles referenced in the Letter will be given the opportunity to respond.

Previous Letters published can be found on the publisher’s website: http://journals.lww.com/nursingresearchonline/ Pages/LetterstotheEditor.aspx.

Nursing Research • November/December 2019 • Volume 68 • No. 6 Stress and Health in Nursing Students 463

http://www.editorialmanager.com/nres/default.aspx

http://journals.lww.com/nursingresearchonline/Pages/LetterstotheEditor.aspx