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SampleTOE-Staffing.pdf

Table of Evidence (TOE) PICO Question: For patients in acute care facilities, how does the number of nursing staff (RN, LVN, CNA) affect the prevalence of patient falls?

Citation (student name)

Kalisch et al., 2011

Purpose “The aim of this study was to determine whether the omission of elements of nursing care (ie, missed nursing care) leads to a greater number of patient falls, using actual fall rates gathered from our study hospitals and controlling for nurse staffing” (p. 6).

Research Question “The related research questions were as follows: (1) Do nurse staffing levels (HPPD) predict patient falls? And (2) Does missed nursing care mediate the effect of staffing levels on patient falls?” (pp. 6-7).

Hypothesis “Missed nursing

Sample Acute care hospitals with bed size ranging from 60-913 from unspecified locations.

Sample Size n= 11

Sampling Method This study utilized convenience sampling.

Setting Specific location not stated in study.

“The study was conducted in 11 acute care hospitals ranging in bed size from 60 to 913. The number of participating patient care units in each of the hospitals ranged from 2 to 22 units, which included a total of 124 units. The total nursing

Research Design Quantitative correlational design

“Correlation analyses were used to address the relationship between staffing, case mix index (CMI), missed nursing care, and patient falls” (p. 9)

Level of Evidence Level IV

Treatment Treatments not applicable to this study.

Controls Unit inclusion criteria were “(1) an average patient length of stay 2 days or more and (2) a patient population older than 18 years.” Exclusion criteria were “(1) short stay

Measurement “Bivariate Pearson correlations were calculated to examine the relationships among HPPD, CMI, missed nursing care, and patient falls” (p. 9)

“To examine the mediating effect of missed nursing care on the relationship between staffing (ie, HPPD) and patient outcomes (ie, fall rates), 3 regression equations were calculated.” (p. 9)

“Data included (1) surveying the nursing staff on each of the study units utilizing the MISSCARE survey and (2) collecting HPPD and fall rate data by patient care unit from administrative data

Findings “Hour per patient day was negatively associated with patient falls (r = −0.36, P < .01). The higher the overall missed nursing care score, the higher the patient fall rates (r = 0.30, P < .01)” (p. 9).

“Results indicated that HPPD was significantly associated with missed nursing care (F1,120 = 8.46, P = .004)” (p. 10).

“Hour per patient day was significantly associated with patient fall (F1,115 = 17.20, P < .001)” (p. 10).

“Missed nursing care negatively affected patient

care was hypothesized in this study as a mediating variable in the relationship between HPPD and patient falls.” (Kalisch et al., 2011, p. 9).

staff on these units who participated in the study was 3432 nurses (RNs and licensed practical nurses) and 980 nursing assistants” (p. 8)

units (≤23 hours) and (2) pediatric, women’s health, perioperative, and psychiatric units.” (p.8)

Variables & Their Definitions IV1: Patient falls DV1: Missed nursing care DV2: Hours per patient day (HPPD)

“Missed nursing care was measured by the MISSCARE survey that asks participants to identify how frequently elements of care (such as, ambulation, turning, patient assessment, teaching, discharge planning, medication administration) were missed, using a 4-point Likert scale, with anchors

repositories. (p. 8).

“For staffing data, hospitals were asked to provide the data in raw form (ie, numerator and denominator) to ensure consistency in computation across hospitals. Administrative staff in each hospital were given a Microsoft Excel file with specific definitions and data requirements and were asked to in- put data into a template designed by the research team. Then, the research team computed all variables of interest. Fall rate data also were collected by administrative staff of each hospital after providing a specific definition of fall rate to the

falls (t = 2.49, P = .014), explaining 9.2% of variance in patient falls” (p. 10).

Conclusions “The results of this study demonstrate that the level of nurse staffing predicted patient falls.”

“Missed nursing care was also found to mediate the relationship between staffing levels and falls. The effect of staffing levels on fall rates is lessened when standard nursing care is completed (ie, no missed care, specifically ambulation, patient assessments each shift, focused reassessment, call light response, and toilet assistance.

“rarely missed” (1) to “always missed” (4).” (p. 8).

“The HPPD values were calculated as the number of productive hours worked by all nursing staff (RN, licensed practical nurse, nursing assistant) with direct patient care responsibilities divided by in-patient days” (p. 8).

“Falls were defined as any event in which patients are found on the floor (observed or unobserved) or an unplanned lowering of the patient to the floor by staff or visitors” (p. 8).

staff. The monthly fall rate was collected for each of the 2 months corresponding with survey administration in each hospital.” (pp. 7-8).

The information in this article is reliable and valid because several procedures were used to ensure consistency, such as providing raw data in numerator/denomin ator form, as well as providing participating hospitals the definitions of the terms used in the study.

The article states that correlation analyses and 3 separate regression equations were

This suggests that one method of preventing patient falls is to devise methods whereby nursing staff complete standard nursing care more so than adding staff.” (pp. 10-11).

used.

Level of significance is p<0.5.

Citation (student name)

Patrician et al., 2011

Purpose “The objective of this study was to demonstrate the association between nurse staffing and adverse events at the shift level” (p. 64).

Implied Research Question Is there a relationship between sufficient nurse staffing and adverse patient events?

Hypothesis “A basic assumption of this study was that adverse patient events occur in the absence of sufficient staffing on that shift” (p. 65)

Sample/Setting Medical-surgical, step down, and critical care units from thirteen military hospitals taken from the Military Nursing Outcomes Database (MilNOD)

Locations not stated.

Sample Size n= 13 (Hospital sample)

Sampling Method Convenience sampling was used.

Hospitals used in the sample were specifically selected based on military affiliation and geographic location.

Research Design Quantitative descriptive design

Level of Evidence Level VI

Treatment/Control s There were no treatments or controls stated in this study.

Variables & Their Definitions IV1: Medical surgical units IV2: Critical care units IV3: Step-down units

DV1: Falls DV2: Falls with injury DV3: Medication administration errors

“Three staffing measures were used. Total nursing

Measurement “Staffing measures were captured for each: day (7:00 AM to 2:59 PM), evening (3:00 PM to 10:59 PM), and night (11:00 PM to 6:59 AM). At the end of every shift, a designated unit staff member entered hours worked by each provider type and staff category into a standardized Microsoft Access database housed on a unit computer.” (p. 66)

“For adverse patient events, the institutional incident reports were reviewed by trained on-site nurses, and data including unit, date, time, and patient harm were extracted and merged with the shift of occurrence”

Findings “A greater proportion of RNs relative to unlicensed assistive personnel (the comparison category) was significantly associated with fewer falls in medical-surgical and critical care units” (p. 67)

“Fewer falls were associated with a higher percentage of DoD civilian nurses working on a shift. Higher nursing care hours per patient per shift were significantly associated with a decreased likelihood of both falls and falls with injury. Increased acuity was associated with increased falls only on medical-surgical

care hours per patient per shift was defined as the sum of hours worked by all nursing personnel during the shift divided by the numbers of patients present at the beginning of the shift.” (p. 65)

“Skill mix was defined as the proportion of hours worked by each skill level of staff (RN, LPN, and unlicensed provider) during a shift.” (p. 65)

“A fall was defined as a patient’s unplanned descent to the floor and further described as with or without injury, depending on whether an injury was sustained, as

(p. 66)

The article has a data quality section that describes the several methods used to ensure reliability and validity. Some methods include having study staff able to verify information, 2 study team members categorizing adverse event data, continually assessing data entry errors, contacting units for unusual numbers, and reconciling outliers.

“Bayesian hierarchical logistic regression modeling was used to examine associations between staffing and adverse events” (p. 64)

units. A higher patient census was significantly related to more falls in both step-down and medical-surgical units. Falls without injury were more frequent on night shift; day of the week was not associated with falls” (p. 67)

Conclusions “The findings of this study support the assumption that adverse events occur during shifts that are staffed with fewer personnel overall and fewer RNs in particular” (p. 67)

“There was a strong relationship between total staffing (nursing care hours per patient shift) and falls with injury”

documented on the incident report, at the time the fall occurred” (p. 65)

“Staff category mix was defined as the proportion of hours worked by each of 4 staff member categories during a shift (ie, active military, Department of Defense [DoD] civilian, contractor, and military reservist) as the military employs 4 distinct categories of staff” (p. 65)

Level of significance p<0.05.

(p. 68)

“This study broke new ground in its methodo- logical granularity and in linking unit-level, shift- specific staffing to key adverse events that are preventable in hospitals and provides strong evidence for continuing discussions examining the relationship of patient outcomes to nursing care” (p. 69)

Citation (student name)

van Rensburg et al., 2020

Purpose “The study aimed to determine the factors that influence patient falls.” (pg. 1)

Research Question (Implied) What intrinsic and

Sample “All the adult falls in all the units in both hospitals over a period of 17 months, namely October 2016 to February 2018, were included in this study…The

Research Design & Treatment Descriptive study design

Level of Evidence: Level VI

Treatment non-applicable for

Measurement “The researcher and research assistant gathered the data on the patient falls from the hospitals’ internal electronic database systems, each patient’s hospital folder, and

Findings Findings Intrinsic factors Median age = 68.37 years Age range = 20-92 years More women than men fell (52.2% vs 47.8%)

extrinsic factors affect the fall rate in South Africa hospitals?

Hypothesis None present Alternative: The physical conditions, environment, nursing levels, and skill mix of a hospital impact the fall rate.

final sample consisted of 134 records.” (pg. 2)

Sampling Method Consecutive sampling The researchers used people admitted to the hospital in the specific units that fell.

Setting “The study was conducted in two private hospitals belonging to the same hospital group in the Cape Metropole, South Africa. Both hospitals are classified as large hospitals with more than 200 beds. Hospital A has 250 acute beds distributed between general surgical, medical, cardio- and neurosurgical,

this study. Controls Two hospitals that belong to same hospital group, classified as large hospitals (200+ beds) Specific wards of the hospital Same data extraction sheet The first author and trained research assistant

Variables IV: Fall rate

DV1: Physical conditions DV2: Environment of patient DV3: Nursing staff levels DV4: Skill mix

from the hard copies of the incident forms” (pg. 3).

Through using direct patient records and analyzing the fall rate through the records, the reliability and validity is high as the data comes directly from the hospital administration records.

Descriptive statistical analysis was used. Results displayed using frequency distributions and measures of central tendency and variance.

Logistical regression used for prediction.

Highest number of falls among general medical patients High relation to falls in patients taking benzodiazepines Extrinsic factors Furniture and equipment 13.4% Wet floors 11.9% Call bell was available to 93.3% of patients Bedrails were not used in 61.2% of fall cases Majority of patient fell next to their bed Staffing skill mix include one RN, two enrolled nurses and enrolled nursing auxiliaries, and one caregiver. Most falls were at night 73.1% had no injuries Women fell more than men Age is a risk factor in falling

orthopedic, pediatric, maternity, intensive care, as well as in high care wards. Hospital B has 200 beds located in medical wards, various surgical wards such as vascular, general and gastro-intestinal wards, pediatric and maternity wards, and intensive care units.” (pg. 2)

Level of significance: alpha < 0.05.

80.6% of patients had one or more comorbidities 60.4% had hypertension 32.5% has sensory disturbances Medication administered prior to the fall contributed to the patient falling – 68.7% were on benzodiazepines 13.4% environmental factors A call bell was available to the majority of patients 97.8% of falls were unassisted Only 50.7% of health assessments were done at admission and shift change in which a fall assessment was not included

Conclusions “The lack of accurate and

consistent patient fall risk assessments, use of benzodiazepines as a sedative and the staff skill mix were contributors to the fall rate in these hospitals” (pg. 1).

Citation (student name)

Whitman et al., 2002

Purpose “This study was conducted to determine the relationships between nursing staffing and specific nurse-sensitive outcomes (central line blood-associated infection, pressure ulcers, fall, medication error, restraint application duration rates (RADR) across specialty units (cardiac and noncardiac ICU, cardiac and noncardiac intermediate care,

Sample “The study was a secondary analysis of prospective, observational data from 95 patient care units (cardiac intensive care, n = 15; noncardiac intensive care, n = 7; cardiac immediate care, n = 18; non cardiac intermediate care, n = 12; medical-surgical, n = 43) across 10 adult acute care hospitals (rural, community, and tertiary) in an integrated healthcare system in

Research Design & Treatment Correlational research design

“Secondary analysis of prospective, observational data from 95 patient care units” (pg. 633).

Level of evidence: Level IV

No treatment applicable for this study.

Controls: type of unit, type of staff, patient days per

Measurement “Staffing hours included the total worked hours (paid hours minus sick, vacation, and holiday hours) for all personnel (RN, licensed practical nurses, nursing aides, secretaries). At the time of the study, the system was unable to remove hours for indirect providers (ie, secretary and nurse manager) for all the hospitals. Therefore, for consistency, these indirect hours were included. WHPPD

Findings “No statistically significant relationships were found between the outcomes of CLI and pressure ulcer rates and WHPPD across the specialty units. An inverse relationship between WHPPD and falls was present in cardiac intermediate care (r = - .53, n = 18, P < .05). Medication error rates were inversely related to WHPPD in the cardiac ICU setting (r = - .55, n = 15, P < .05) and

and medical–surgical).” (pg.634-635)

Research question (Implied) Is there a relationship between nursing staffing and specific nurse-sensitive outcomes (central line blood-associated infection, pressure ulcers, fall, medication error, restraint application duration rates (RADR) across specialty units (cardiac and noncardiac ICU, cardiac and noncardiac intermediate care, and medical–surgical)?

Hypothesis None present Alternative: There will be a

the eastern United States” (pg. 635).

Sampling Method Consecutive sampling as they used patients in a hospital.

“WHPPD were obtained by dividing total worked hours by the monthly patient days for each unit. Standardizing by patient days controlled for the occupancy and size of the units. Monthly staffing hours and patient days per unit cost center were obtained from the system’s finance department” (pg. 635)

Setting 10 acute care hospitals (rural,

unit Variables IV1: Staffing levels

DV1: CLASBI DV2: Pressure ulcer DV3: Falls DV4: Medication error DV5: Restraint application DV6: Worked hours per patient day

were obtained by dividing total worked hours by the monthly patient days for each unit. Standardizing by patient days controlled for the occupancy and size of the units. Monthly staffing hours and patient days per unit cost center were obtained from the system’s finance department” (pg. 635).

Data collection tool to pull data from the charts.

Because the researchers used the records from the hospital, the data is very reliable and valid for this study.

Descriptive statistics – means and standard

noncardiac intermediate care (r = -.65, n = 12, P < .05). RADR was inversely related to WHPPD only in the medical–surgical units (r = - .48, n = 12, P < .01). The coefficient of determination between WHPPD and medication error in the cardiac ICU was .303 (ie, r2 = (-.55)2, indicating that 30.3% of the variance in medication error rates could be explained by the variance in WHPPD scores. Similarly, variance in WHPPD accounted for 42.3% of the variance in medication error rates in noncardiac intermediate care; 28.1% in the

relationship between nursing staffing and specific nurse-sensitive outcomes

community, and tertiary) in the eastern United States

deviation Pearson parametric and Spearman’s rho nonparametric correlational methods

Level of significance < 0.05

falls in the cardiac intermediate care; and 23.0% in RADR in the medical–surgical units. Although not reaching levels of significance, trends were also noted for inverse relationships between falls and WHPPD in the noncardiac ICU (r = -.68, n=7, P = .09) and for medication error rates and WHPPD in cardiac intermediate care (r = - .45, n = 15, P = .09).” (pg. 636).

Conclusions “Results from this study suggest that the impact of staffing on outcomes is highly variable across specialty units; however, when present, the

relationships are inversely related with lower staffing levels, resulting in higher rates of all outcomes” (pg. 633).

Citation (student name) Kim et al., 2019

Purpose “The objective of this study was to investigate both individual and organizational factors influencing the falls of patients in hospitals” (p. 1).

Research Question And/Or Hypothesis How has the implementation of integrated care in Korea affected patient safety outcomes?

Sample/Setting “This study included patients who were admitted to integrated care units from April 1, 2017, to June 30, 2017. We included only patients who were hospitalized and discharged during the period and excluded those with insufficient data. A total of 275 hospitals with integrated care units in 2017 were included in this study. A total of 60,049 patients were admitted to such units during the study period” (p. 3).

Research Design & Treatment This was correlational research with a retrospective design. “Patients who were admitted to integrated care units were retrospectively enrolled” (p. 2). The researchers compiled their data from results that they received from the hospitals directly.

Level of evidence: Level IV

As this is not an experimental study and the researchers

Measurement “The nursing needs scores were assessed as applicable or to meet a given criterion. Therefore, in the case of a patient with multiple conditions, the score would be high. The scores ranged from 0 to 10. Activities of daily living dependency was assessed in four areas: position in bed, ambulating, toileting and feeding. Scores were either free (score 0),

Findings Patient data: Average age was 61 years, with a higher ratio of females to males. The average length of hospital stay was 7.1 days.

Hospital data: “Registered nurses staffing at high levels was 1:5, 1:7 or 1:8 and 1:10 in tertiary hospitals, general hospitals and semi-hospitals, respectively” (p. 4). Nursing assistants staffing had only three standards (1:25, 1:30 and 1:40). Since RN staffing in tertiary hospitals

This study took place in Korea.

were observing results from past patients, there was no treatment or intervention in this study.

Controls: This study recognized the factors that may increase falls such as patient’s age, diagnosis, mobility impairment, and psychological factors.

Variables & Their Definitions DV- Falls - “the fall rate as falls per 1,000 patient days” (p. 3)

IV 1- Hospital data- “included hospital type, hospital size, ownership and staffing of RNs and NAs” (p. 3)

IV 2- Patient data- “included the

partially dependent (score 1), or fully dependent (score 2). Therefore, the scores for ADL dependency ranged from 0 to 8” (p. 3)

Reliability and validity: “A total of 275 hospitals with integrated care units in 2017 were included in this study” (p. 3). Since this study used data submitted by 275 hospitals, it is likely that if the research was to be done again, there would be similar results. With that many hospitals, any outliers would not have as large of an impact as there would be if they studied a smaller number of hospitals. This makes the study

was higher than that in other hospitals, NA staffing in tertiary hospitals was relatively low” (p. 4).

Health data: “The fall rate was 0.92 per 1,000 patient days in this study. By type of hospital, the fall rate was the highest in general hospitals (1.28 cases per 1,000 patient days), and the lowest in tertiary hospitals (0.47 cases per 1,000 patient days)” (p. 4).

Conclusions “Age, mobility problems and RN staffing were found to be significant factors influencing patient falls. To prevent falls, these risk factors should be

following: age, gender and the reason for hospitalization” (p. 3)

IV 3- Health data- “included patients’ health status and falls, collected daily from admission to discharge” (p. 3)

more reliable. When looking at the table on page 5, the data is present for all of the variables that the study set out to measure. The researchers were able to collect the data that was necessary to answer their research question.

Statistics: “We used a chi-squared test and ANOVA” (p. 3).

Level of significance: When looking at the table on page 5, the p value is listed as 0.000.

considered and prevention strategies constructed accordingly. Because age and ADL dependency are unchangeable factors, RN staffing needs to be set at an adequate level” (p. 8).

Citation (student name) Staggs & Dunton, 2014

Purpose “To enhance understanding of how nurse staffing relates to unassisted falls by exploring

Sample/Setting “The final sample comprised 1557 step-down, 2010 medical, 2567 medical–surgical,

Research Design & Treatment This was correlational research with a retrospective

Measurement RN HPPD = total RN hours divided by total patient days.

Findings “There were 203 094 patient falls reported during the 57 518 290 patient days in

non-linear associations between unassisted fall rates and levels of registered nurse (RN) and non-RN staffing on 5 nursing unit types, thereby enabling managers to improve patient safety by making better-informed decisions about staffing” (p. 1).

Research Question And/Or Hypothesis How is nurse staffing related to the rate of unassisted patient falls?

1395 surgical, and 540 rehabilitation units in 1332 general hospitals and 29 rehabilitation hospitals” (p. 2).

“There were 203,094 patient falls reported” (p. 3).

“We extracted monthly unit-level data on nurse staffing and in- patient falls for 2011 from the NDNQI. The NDNQI collects data on nursing-related measures from over 1900 U.S. hospi- tals (about one-third of all U.S. hospitals). Neither the study sample nor the larger set of NDNQI hospitals is a random sample of hospitals;

design. “We extracted monthly unit-level data on nurse staffing and inpatient falls for 2011 from the NDNQI” (p. 2). The researchers retrospectively gathered all of their data from this national database.

Level of evidence: Level IV

There was no treatment in this study. This was a non-experimental study in which the researchers were studying data that had been submitted by hospital units. Since it was a retrospective non-experimental study, they did not have an intervention.

Controls:

Non-RN HPPD = total non-RN hours divided by total patient days.

Unassisted falls reported per 1000 patient days.

Percent of falls reported as unassisted

Reliability and validity: The researchers for this study received their data from the National Databases of Nursing Quality Indicators (NDNQI). This is a reputable place for them to get their information from and increases the chances that they would get similar results if they were to repeat this study. Their purpose for doing this study was also met as a

the study (3.53 falls per 1000 patient days). Of these falls, 171 792 (84.6%) were unassisted, and 27 167 (13.4%) were assisted, the remainder being unclassified” (p. 3).

“There was a linear association between RN staffing and the unassisted fall rate for medical–surgical units. Holding other predictors constant, the estimated average fall rate decreased by 2% (95% CI: 0–3%) per additional RN HPPD” (p. 5).

Conclusions “Our analyses confirm that the association between RN staffing

participation in NDNQI is voluntary, and not all participating hospitals choose to submit data for all eligible nursing units” (p. 2).

“We included hospital bed size and teaching status as control variables in all models” (p. 3).

“One limitation of this study is that we could not control for patient acuity (other than by treating each unit type separately) or for patient characteristics such as age” (p. 5).

Variables & Their Definitions IV 1- RN Staffing levels- “At the unit level, RN HPPD was defined as the sum of nursing care hours provided by RNs during the month divided by the sum of the unit’s patient days for the month. As defined by NDNQI,

result of the data that they collected and analyzed from NDNQI.

Statistics: “We analyzed the associations between staffing and unassisted fall rates using three-level generalized linear mixed models” (p. 2-3).

“We used restricted cubic splines to model non-linear associations between the unassisted fall rate and the two staff- ing variables. The cubic spline model is a well-established tool for fitting a curve to capture a highly non-linear association between a predictor and response variable” (p. 3).

and the rate of unassisted falls varies by unit type. On step- down and medical units, the association between RN staffing and fall rates depended on the level of staffing: at lower staffing levels, the fall rate increased as staffing increased, but at mod- erate and high staffing levels, the fall rate decreased as staffing increased” (p. 5).

nursing care hours include only productive hours provided by nursing employees who are assigned to a specific unit and spend more than half their shift in direct patient care” (p. 2).

IV 2- Non-RN Staffing levels- “Non-RN HPPD was defined in the same way using nursing care hours provided by LPNs and assistive personnel” (p. 2).

DV - Falls - “The dependent measure was the number of unassisted falls per patient day as reported monthly for each unit” (p. 2).

“Modeling was carried out using the GLIMMIX Procedure in SAS 9.2” (p. 3).

Level of significance: “Non-RN HPPD did not have a significant non-linear association with the unassisted fall rate for any unit type. The RN HPPD spline contrast was significant only for step-down (P-value = 0.040) and medical units (P-value = 0.017)” (p. 3).

Citation Purpose Sample/Setting Research Design Measurement Findings

(student name) Breckenridge et al., 2012

“The purpose of this article is to examine the influence and relationship of nurse staffing and workload factors on medication errors and patient falls at the unit level between 2003 and 2006 in Army hospitals and to explore the effect of the practice environment on these relationships” (pg. 457).

Research Question And/Or Hypothesis How does the staffing of nurses contribute to adverse patient outcomes?

Sample size = 506 nurses and 23 units, with a census ranging from 44-165 ( pg. 461).

The sample consisted of nurses that were working in army hospitals and the patients that they were caring for (pg. 458).

The sampling method used was convenience sampling because the researchers used patients in these hospitals since they were convenient sources listed in the data set (pg. 458).

The setting of this study was in medical-surgical units, step-down units, and critical care units in the Continental US (pg. 458).

& Treatment The research design used was a correlational design (pg. 468).

This study is Level IV of evidence.

The controls used were restricting the data to the four army hospitals, and only using the 2006 survey so as to avoid “presumed differences” and prohibit the same individuals to repeat in the data collection (pg. 458-459).

Variables & Their Definitions DV#1 - Medication Errors: Alteration from a doctor’s in regards to medication administration, done by a licensed

Staffing measures: “hours worked by direct care providers of each skill level and staff category” (pg. 459).

Acuity: how serious the patients’ medical states were

Census: the amount of patients on a unit, which was measured each day at the same time

Reliability/Validity - “The MilNOD parent study conducted several assessments to support data reliability and validity”(pg. 460). “The Cronbach’s alpha internal consistency reliability for the entire PES alpha was .92” (pg. 459).

“Both medication error and fall rates were highest in medical-surgical units and lowest in the critical care units” (pg. 466)

“Two control variables, study duration in months and higher acuity, were significantly associated with a higher rate of medication errors but only for medical-surgical units. In critical care units, a higher percentage of LPNs was associated with a higher medication error rate” (pg. 460).

This could be due to the fact that in medical-surgical units the patients

nurse (pg. 460).

DV #2 - Falls: “a patient’s unplanned descent to the hospital floor” (pg. 460).

IV#1: Staffing - the characteristics and practice of the staff (pg. 460).

Level of Significance: P = > .05

are more ambulatory, thus increasing their chance of falling, and there is a higher nurse-to-patient ratio in ICUs.

Conclusions “In this study, neither total nursing care hours nor RN skill mix was a statistically significant predictor of medication errors or falls” (pg. 466).

“This study revealed that among the structural variables, staff category and acuity were associated with medication error and fall rates” (pg. 466).

“The results indicated that a less favorable practice environment rating is associated with

an increase in medication errors in all types of units. However, the practice environment did not significantly mediate falls” (pg. 468).

Citation (student name) Twigg et al., 2016

Purpose To analyze the impact of adding nursing assistants to hospital units in acute care on adverse patient results.

Research Question And/Or Hypothesis Having nursing aids will help to lessen the nurse’s work load, thus it will allow the nurse to focus better on

Sample/Setting The sampling method used was purposive sampling because the researchers selected subjects that would be most useful for the study’s purpose. The study took place in Western Australia (pg. 191).

“Patients were retained in the dataset if they spent any time on a medical, surgical or rehabilitation ward during their

Research Design & Treatment “This study used a quasi-experimental pretest-posttest control group design” (pg. 191).

Treatment: the use of AINs

Controls: “variable that indicated the proportion of time the patient spent on low skill mix wards” since this is a common cause of poor outcomes for

Measurement Patient outcome was scaled outcome frequency; by whether or not there was the presence of failure to rescue, mortality, UTI, pressure injury, pneumonia, sepsis, or falls with injury (pg. 198).

Reliability/Validity - These results are only valid for this population of patients, therefore it was not repeated consistently and it

Findings Wards with AINs typically had more negative patient outcomes. Therefore AINs might have been used in staffing to try and even out the lack of RNs, but they are not able to provide the same care RNs can, therefore care issues can arise (pg. 199). Mortality rates and failure to rescue rates fluctuated but had a downward trend for

patient care and project into positive patient outcomes.

admission and excluded if they only spent time on other ward types, as the outcomes used in this study are only validated for these patient populations. There were 256,302 patient records in the total sample with 125,762 in the pre-test period (2006–2007) and 130,540 in the post-test period (2009–2010)” (pg. 193).

patients (pg. 193).

This study is Level III of evidence because it is quasi-experimental.

Variables & Their Definitions IV #1 - AIN Wards and these characteristics: age, gender, season of admission, indigenous status, referral source (to hospital), Diagnosis Related Group, cost weight, age squared, peer group of hospital, length of stay, type of admission (elective or emergency), and Charlson comorbidity index

IV #2 - same as IV #1 but for Non-AIN wards

DV #1 - mortality rates

cannot be said it represents all patients (pg. 191).

This study used means, as well as frequencies. A chi-square test was also used to determine the level of significance for expected and observed frequencies of outcomes (pg. 193).

Level of Significance: P = > .05

units that had AINs. However, urinary tract infection rates and pressure injury rates fluctuated but had an upward trend for the units with AINs (pg. 194-195).

Conclusions The scope of nursing assistants should be clear and consistent in order to provide safe care and avoid adverse patient outcomes.

“The results from the pre-test/post-test analysis (Table 3) showed that there were three significant increases in adverse outcomes on the AIN wards (failure to rescue, UTI, falls with injury) when comparing the observed to

DV #2 - failure to rescue rates

DV #3 - urinary tract infection rates

DV #4 - pressure injury rates

expected number of outcomes, with one significant decrease (mortality)” (pg. 193).

“On the non-AIN wards there was one significant decrease (pneumonia) in the observed to expected adverse outcomes and one significant increase (falls with injury), with three other non-significant increases and two non-significant decreases in adverse outcomes” (pg. 194).

Citation (student name) Assaye et al., 2022

Purpose “The aim of this study was to measure the level of missed nursing care and determine its

Sample/Setting “The study was conducted in two hospitals (one public and one private) in Amhara

Research Design & Treatment The research is quantitative observational design.

Measurement Asking the nurses to rate the level of rationing of each intervention during their week. This

Findings “The age of participants in TP2 ranged from 20 to 56 years. Whereas the majority of

relationship with nurse staffing and patient safety outcomes in acute care settings in Ethiopia” (Assaye et al., 2022, p. 1)

Research Question And/Or Hypothesis What is the level of missed nursing care and its impact in low-income countries?

Regional State, Ethiopia.” (Assaye et al., 2022, p. 3)

“Medical records of patients who were aged 18 years or above; were admitted to the medical, surgical or orthopedics units of study hospitals; and had stayed in the unit for more than 2 days were included in the study.” (Assaye et al., 2022, p. 3)

This study conducted a purposive sampling method.

“Regarding sample size determination, all nurses who had worked in the medical, surgical and orthopedics units of the study hospitals for at least 3 months were

The level of evidence for this study is VI.

In this study a survey was given out to the nurses.

Controls: “ Study participants included nurses working in the medical and surgical units of the study hospitals as well as patients receiving care in the study units at the time of data collection. Nurses of any level who had worked in the study units for at least 3 months and for whom at least 50% of their responsibility involved direct patient care were eligible for inclusion in the study….. only

rating was an ordinal measurement because 0 = not required, 1 = never, 2 = rarely, 3 = sometimes, and 4 = often.

The medical records of patients and their hospital acquired, UTI, pressure injury, HAP, and mortality were included.

Reliability and validity: This study is reliable and valid when comparing data to low income countries due to the similar population.

This study used the ANOVA, simple linear regression, and T test to identify the independent predictors of MNC. Variables with

participants (75.9%) had a Bachelor's degree, only 1.3% had a Master's degree. Forty-six (59.7%) participants had work experience of 1 to 5 years. No participant from the private hospital reported work experience greater than 10 years (see Table 1). There were no significant variations in relation to age, gender, educational status and years of work experience of the participants in TP1 and TP2.”(Assaye et al., 2022, p. 5)

“The multivariate analysis showed a statistically significant association between both the type of hospital units and

considered for inclusion in the study. At the first point, all 126 nurses in the study units were invited to participate and 74 responded (59% response rate). For the second time point, 80 nurses of 125 working in the study units (64% response rate) completed the survey.” (Assaye et al., 2022, p. 3)

“a medical record review of 517 patients in four units across two hospitals between September 2018 and March 2019.” (Assaye et al., 2022, p. 1)

registered nurses were included in the study. (pg3)

Variables & Their Definitions

IV1: Registered Nurses: Only registered nurses were included in this study who worked in the unit for at least 3 months.

IV2: Hospital unit: The units that are chosen of where the survey will be given. (Medical, surgical, and orthopedic)

DV1: Missed Nursing care (MNC): omission of important patient care. (answered through survey questions)

DV2: Patient safety

results P ≤ 0.25 were entered into the final multivariate model.

Level of significance: P < 0.05

the hours nurses worked per week with the mean MNC score at TP2 (see Table 3). The mean MNC score in the medical-surgical unit of the private hospital was significantly lower than the mean MNC score in the surgical unit of the public hospital (B = 0.812, P = 0.002). The mean MNC in the medical unit and orthopedics unit was significantly higher than the mean MNC score in the surgical unit (see Table 3). Working more hours per week was also significantly associated with a reduction in the level of MNC (B = 0.021, P = 0.032). A 1-h increase in the hours nurse

outcomes: the outcomes of the care the patient receives from the nurse and how safe they are in the nurses hands.

worked in their last week was associated with a reduction of MNC by 0.021 (P = 0.032)”(Assaye et al., 2022, p. 5)

“The findings demonstrate there was a statistically significant association between MNC score and the incidence of at least one of the four adverse patient safety outcomes……A lower level of MNC score was also significantly associated with ‘improved’ discharge status of the patients.” (Assaye et al., 2022, p. 5) Conclusions Mechanisms are needed to improve staffing to enable nurses enough time

to provide nursing care. Also there is a higher level of MNC in the medical and surgical units than in higher income countries.

Citation (student name) Dunton et al., 2004

Purpose “The principal aim of this paper was to estimate the effect of three components of nurse staffing on patient falls for specific types of units in acute care hospitals” (Dunton et al., 2004, p. 54)

The 3 concepts are: -Nursing care -Skill mix -Continuity of the staff

Research Question And/Or Hypothesis -“More nursing

Sample/Setting The United States “NDNQI data set for fourth quarter 2002, there were 282 facilities: 12% had fewer than 100 beds, 26% had between 100 and 199 beds, 17% from 200 to 299, 16% from 300 to 399, 11% from 400 to 499 and 18% with 500 plus beds. There were a total of 1,836 units in the data set: 25% critical care, 17% step-down, 19% medical, 14% surgical and 26% combined medical

Research Design & Treatment The research design in this study is a quantitative correlational design.

The level of evidence for this study is VI.

Controls: From the vast database, three concepts were represented to specify what is needed.

Variables & Their Definitions IV1: Nursing care: How much care

Measurement “The total amount of nursing care received by patients on a unit (total nursing hours per patient day), the skill mix represented in those hours (percent of nursing hours supplied by RNs), and the continuity of staff (percent of nursing hours supplied by contract nurses)Fall data were obtained from incident reports. Facility size was measured by the number of staffed beds in the

Findings –“The first staffing variable was the number of nursing hours per patient day (nhppd), with medians ranging from 15.9 for critical care units to 7.5 for medical units. The mean percentage of total nursing hours supplied by RNs (%RN) ranged from 89.2% on critical care units to 60.6% on combined medical surgical units. The percentage of nursing hours supplied by contract

hours per patient day will be associated with fewer patient falls (and fewer injury falls) per 1,000 patient days in acute care hospitals.”(Dunton et al., 2004, p. 54) -“A higher percentage of RN staff will be associated with fewer patient falls (and injury falls) per 1,000 patient days.” (Dunton et al., 2004, p. 54) -“A higher percentage of contract nursing staff will be associated with more patient falls (and injury falls) per 1,000 patient days.” (Dunton et al., 2004, p. 54) -“Relationships between nurse staffing and patient falls will be

surgical. The data for this analysis came from NDNQI for the fourth quarter (October through December) of 2002. Data on nurse staffing and patient falls were reported for 2,351 units by 282 hospitals in 45 states. Participation in the NDNQI is voluntary, and thus, the analysis was based on a convenience sample” (Dunton et al., 2004, p. 54)

was given to a patient

IV2: Skill mix: different RN caring for the patient

IV3: Continuity of the staff: unbroken continuous work from the staff.

DV: Falls data: the amount of falls that were recorded in the hospital.

hospital.”(Dunton et al., 2004, p. 55)

Reliability and Validity: Since the database the study was based on encompasses several hundred facilities in the United States and many of the hospitals agreed to be a part of the study, it may be replicable.(p55)

The generalized mixed model was used in this study (p56)

Significance: “ For illustrative purposes, the figures show the relationship for the most common settings— units with 60% registered nursing hour’s effort, 5% contract nursing effort, and a

staff ranged from 6% on step down units to 4% on medical units.” (Dunton et al., 2004, p. 56) –“During the fourth quarter of 2002, NDNQI hospitals reported on 13,134 falls.b Falls occurred on all unit types, but were most common on medical units and least common on critical care units. Approximately 30% of the falls involved an injury. There was little variation in prevalence or severity of injury by unit type. The mean number of falls per 1000 patient days was 3.73. The mean number of injury falls per patient day was 0.99.” (Dunton et al., 2004, p. 56)

stronger for medical units than for step-down or surgical units.”(Dunton et al., 2004, p. 54)

hospital whose bedside was 300–399.”(p57)

p-value<.05 also shown in table 3.

–“The percentage of registered nursing hours had a significant negative association with the number of falls for step-down and medical units, but was not significant for surgical and combined medical-surgical units. The percentage of contract nursing hours had a significant positive association with the number of falls for medical-surgical units, but not for the other unit types” (Dunton et al., 2004, p. 57)

Conclusions “Fall rates were highest on medical units, and the relationship between fall rates and nursing hours was strongest on

medical units. Thus, targeting increases in nurse staffing to medical units would have the greatest opportunity to reduce patient falls, through higher levels of monitoring and assisting mobile patients. “ (Dunton et al., 2004, p. 58)

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