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. 2024 Oct 6;34(6):2324–2335. doi: 10.1111/jocn.17437

Understanding the Experiences of Nurses' Work: Development and Psychometric Evaluation of an End of Shift Survey

Jenny M Parr 1,2,✉, Julia Slark 1, Jane Lawless 3, Stephen T T Teo 4
PMCID: PMC12125536  PMID: 39370546

ABSTRACT

Aim

To explore and validate an end of shift survey with a low response burden, practical application and generated evidence of related associations between workload, quality of work and patient care, missed care and job satisfaction.

Design

A retrospective cross‐sectional survey of the experiences of nursing staff.

Methods

Data were collected from 265 nurses who responded to a questionnaire at the end of their shift in 2022. Exploratory factor analysis was undertaken using IBM SPSS v.27 and confirmatory factor analysis was undertaken using IBM AMOS v27. Hypotheses testing was undertaken using IBM SPSS v.27 using multiple regression analyses.

Results

All of the hypotheses were supported. There was a negative association between workload and quality of work and job satisfaction. Quality of work was negatively associated with workload and missed care and positively associated with job satisfaction. The association between missed care and job satisfaction was negative.

Conclusion

The EOSS is a valid and reliable tool with a low response burden. The tool supports previous research which demonstrated there is a negative relationship between level of workload and shift type with satisfaction, quality of work and potentially nurse retention.

Implications for the Profession and/or Patient Care

In the context of a global nursing shortage nursing leaders must ensure that care we provide is of the highest quality. We must take every action to address high workload to reduce the risk that fundamental care is not sacrificed, job satisfaction is improved and nurses remain in the profession. The EOSS gives nurse leaders a reliable, practical, consistent, applied tool that will better enable associations to be observed between resource configuration, workload and critical impacts on nursing and patient care.

Reporting Method

We have adhered to the relevant EQUATOR guidelines using the STROBE reporting method.

Patient or Public Contribution

No Patient or Public Contribution.

Keywords: end of shift survey, evaluation, job satisfaction, missed care, nursing, quality of care, rationing, workload

Summary.

  • The EOSS captures data from a ‘shift in action’.

  • The EOSS is a valid and reliable tool and has a low response burden, which demonstrated significant relationships between workload, quality of work, missed care and job satisfaction.

  • Nurses can provide their experiences of their workload, quality of work, missed care and job satisfaction. Nursing leaders must show commitment to heed and act on the results of the EOSS.

1. Introduction

Shortages in the global nursing workforce are serious and predicted to worsen. The State of the World's Nursing (2020) report predicted that by 2030 there will be a shortage of 5.7 million nurses. A further 4.7 million nurses globally are expected to retire by 2030) (World Health Organization 2020). Internationally, rates of vacant nursing positions and excessively long durations to recruit are becoming the norm, exacerbated by the COVID‐19 pandemic. Between 2019 and 2022, the United States lost 100,000 nurses from practice (American Hospital Association 2023). The United Kingdom reported to have 40,000 registered nurse vacancies, Germany have over 15,000 long‐term care and 12,000 acute care nurse vacancies taking on average 174 days to recruit hospital nurses (Buchan and Catton 2020). However, this is predicted to rise to over 140,000 in England by 2030 (Shembavnekar et al. 2022).

Retention and recruitment of nurses has never been more important. It is imperative that hospitals have the means to measure, understand and address the experience of nurses in relation to modifiable factors to preserve the workforce we have. Gaining these insights enables nurse leaders to build the reputation of their organisation as one that is interested in its people, and to ensure the profession is seen as an excellent career choice.

Two jurisdictions, New Zealand and the United Kingdom, have attempted to create and implement national standards detailing the components to assure safe staffing (Crossan 2006; National Institute for Health and Care Excellence 2014a). Both of these national programmes have similar principles, including mechanisms to set nursing budgets to meet patient needs and evaluate their effectiveness.

The United Kingdom published NICE Guidance for Safe Staffing which considered evidence regarding the NZ experience (National Institute for Health and Care Excellence 2014b). A suite of safe nursing indicators based on expert opinion were described as sensitive to and applicable for determining whether the staffing adequately meets the patient's nursing needs (National Institute for Health and Care Excellence 2014a).

In New Zealand, commitments to safe staffing and healthy workplaces have been pursued since negotiations of the Multi‐Employer Collective Agreement (MECA) for nurses between 2004 and 2006 led to the establishment of a Safe Staffing Healthy Workplaces Committee of Inquiry (COI) (Crossan 2006). The inquiry established that for staffing to be ‘safe’, ‘appropriate staffing will meet patient care requirements, achieve good health outcomes for patients and ensure that workplaces are healthy and satisfying for staff’ (p. 8). Among its recommendations, the COI also signalled the need for an effective staffing system to collect and use patient acuity data and measurement of staffing effectiveness against patient workload, quality indicators and patient satisfaction (Crossan 2006). An end of shift survey (EOSS) was introduced (Safe Staffing Healthy Workplaces 2023). It needed to be easy to complete at the end of a shift and sensitive to action to address drivers of quality care and job satisfaction such as workload and missed care, and to complement acuity data. The EOSS was to provide retrospective information measuring missed care, staff satisfaction and engagement to inform future planning and workload management.

Nursing leaders want valid and reliable measures of workload, quality of work and care, missed care and staff satisfaction to understand the nurse's experience of work and balance patient acuity demand measures. These measures need to be practical and have a low response burden (Hamilton et al. 2017). A literature review undertaken in 2020 identified a number of tools used in research measuring workload (Bazazan et al. 2019; Tubbs‐Cooley et al. 2018), job satisfaction, demands and resources (Broetje, Jenny, and Bauer 2020; Han, Trinkoff, and Gurses 2015; McVicar 2016; Moloney et al. 2018), missed care and quality of care (Aiken, Clarke, and Sloane 2002; Parr, Teo, and Koziol‐McLain 2021). A one‐item tool, the professional assessment of optimal nursing care intensity level, is in use in Finland (Fagerström and Rainio 1999; Rauhala and Fagerström 2004); however, no comprehensive validated tool capturing workload, quality of work and patient care, missed care and job satisfaction at the end of a shift is in use in practice globally.

The relationship between workload, staffing adequacy, burnout, patient safety and retention is widely acknowledged (National Institute for Health and Care Excellence 2014a; Sochalski 2001; Stimpfel, Sloane, and Aiken 2012; World Health Organization 2020). Studies have shown that care is left undone less frequently in hospitals with (a) more favourable work environments, (b) lower nurse to patient ratios and (c) lower proportions of nurses undertaking non‐nursing tasks (Ausserhofer et al. 2014). Planning and communication was missed more often than clinical care (Griffiths et al. 2018b). Based on the factors most commonly measured by researchers, the most frequent nursing care activities left undone include emotional and psychosocial support, comforting/talking with patients and educating patients and families (Griffiths et al. 2018b), known as fundamental care (Feo et al. 2017).

Stimpfel, Sloane, and Aiken (2012) found that burnout and intention to leave increased with shift length, and working overtime and workload resulting in unfinished work was related to poorer perceptions of quality of care (Sochalski 2001). This reflects findings of research undertaken where associations were demonstrated where the frequency of unfilled roster gaps, inappropriate skill mix for acuity, numbers of patients exceeding number of beds, ability to take breaks and need to work overtime, occurred roughly twice as often when respondents indicated work effort was ‘too hard’ versus ‘about right’, and roughly three times as often when respondents indicated work effort was ‘exhausting’ versus ‘about right’ (Lawless and Bowden 2013). More experienced nurses were less likely to leave (Burmeister et al. 2019). Nurses intending to leave increased the odds of commonly missing nursing care by 2.33 times (Haftu et al. 2019). Day shift nurses were six times less likely to commonly miss nursing care as compared to those who work the night shift (Haftu et al. 2019). An EOSS to be used in practice needs to be practicable, with a low response burden (Hamilton et al. 2017) to encourage regular completion by nurses, and predict outcomes such as quality of care, missed care and job satisfaction.

1.1. Theoretical Background

To understand and hypothesise the relationships between workload, quality of work and patient care, missed care and job satisfaction, Lazarus and Folkman's Transactional Model of Stress is used as the theoretical basis (Lazarus and Folkman 1984). Shifts where staffing is not matched to patient demands contribute to stress and require the nurse to use adaptive coping strategies (Bazazan et al. 2019; Leveck and Jones 1996; Lorente, Vera, and Peiró 2021). Organisational factors (e.g., interpersonal relationships at work, work life quality, occupational stress, occupational satisfaction, organisational commitment, occupational burnout, leadership style, moral distress, work–family conflicts, unsuitable work conditions, as well as violence and justice at work) were correlated with nurses' intention to leave (Taghadosi and Nabizadeh Gharghozar 2019). Stressors such as workload demands are evidenced to be an antecedent of quality of work, including items related to missed care and quality of care (Aiken, Clarke, and Sloane 2002; Ausserhofer et al. 2014; Duffield et al. 2011; Spence Laschinger and Leiter 2006) and associated with job satisfaction (McVicar 2016).

Lazarus and Folkman (1984) define stress as ‘exposure to stimuli appraised as harmful, threatening or challenging, that exceeds the individual's capacity to cope’. Inherent in this model is the constant cognitive appraisal of environmental stimuli which cause stress or distress, and subsequent strategies to cope which may be emotional or problem focused. The intensity of the stress reaction, is mediated by the appraisal and the meaning associated to it (Biggs, Brough, and Drummond 2017). Appraisal is primary and secondary in nature and it is the perception that the event is stressful, rather than the event itself which stimulates the coping strategy.

The primary appraisal leads to a determination of the significance of the event on personal well‐being (Biggs, Brough, and Drummond 2017). This may be benign, irrelevant or harmful. In the case of a nurse in a short‐staffed situation, they may consider their workload as a continuum between manageable and unsafe. The secondary appraisal leads to coping strategies to address the stressful event which shape, manage or resolve the event (Biggs, Brough, and Drummond 2017; Dewe and Cooper 2007). It is guided by the complexity and interaction between the individual's experience of similar situations, their own coping resources, situation and coping style. Coping strategies are either problem focused (aimed to manage the stressor) or emotion focused (regulation of the emotions caused by the stressor). This leads to a re‐appraisal of the situation to determine if the strategies were successful (Lazarus and Folkman 1984). Re‐appraisal occurs with new information, and if the coping strategy was successful, confirms if the significance of the event has changed to benign, irrelevant or harmful to their personal well‐being (Lazarus and Folkman 1984). The frequency of the situation, failure of their coping strategies and negative experiences further influences coping strategies (Healy and McKay 2000).

The COVID‐19 pandemic posed significant and real threat of harm to healthcare workers. In their research with Spanish nurses, Lorente, Vera, and Peiró (2021) demonstrated that planning and instrumental support coping strategies mediated the relationships between work overload, insufficient preparation, fear of infection and psychological distress. Emotion focused strategies such as acceptance, reframing and emotional support, were positively and significantly associated with resilience, but planning strategies were not. Job satisfaction was negatively associated with workload, particularly mental demand and frustration (Bazazan et al. 2019). Physical workload and emotional demands influenced retention of nurses in aged care settings (Clausen, Tufte, and Borg 2014).

We focus on the constructs of workload, quality of care, missed care and job satisfaction using the lens of the Transactional Model of Stress.

2. The Study

2.1. Aim

The aim of the research was to explore and validate an EOSS which had a low response burden, practical application and generated evidence of related associations between workload, quality of work and patient care, missed care and job satisfaction. The study constructs and their relationships (Figure 1) are hypothesised in the following section.

FIGURE 1.

FIGURE 1

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Hypothesised model.

2.2. Constructs and Hypotheses

In this section, we explain the constructs being explored in depth in relation to Lazarus and Folkman (1984) Transactional Model of Stress. The evidence supporting the hypothesised relationships of the conceptual framework is presented with the relevant hypothesis.

2.2.1. Workload

Workload can be described as ‘the discrepancy between the amount of tasks and the time available to perform these tasks in a satisfactory manner’ (Teoh et al. 2023, p. 17). The quality of the practice environment has been related to emotional exhaustion (Kutney‐Lee et al. 2016) intention to stay (Al‐Hamdan, Manojlovich, and Tanima 2017) and job satisfaction (Wargo‐Sugleris et al. 2018). In their study, Aiken et al. (2008) found hospitals with poor environments had lower staffing levels and these aspects significantly contributed to burnout and job satisfaction, and a lower perception of quality of care. Burnout was related to the work environment and patient to nurse ratio (Liu and Aungsuroch 2018). Greater nurse‐to‐patient ratio was consistently associated with higher degree of burnout among nurses, increased job dissatisfaction and higher intent to leave (Shin, Park, and Bae 2018).

Perceived staffing adequacy and job satisfaction were associated with intention to leave (Burmeister et al. 2019). Job demands require sustained physical or mental effort with associated physiological and psychological costs (Demerouti et al. 2001) and are characteristics of the practice environment such as workload, driven by the influence of staffing on mental and physical demands, the pace of the shift, quality of care delivery and patient safety can be perceived as a cause of stress. The most common job demands related to job stress and job satisfaction were workload, staffing, physical resources and the responsibility associated with care (McVicar 2016) and lack of time was associated with necessary care being missed (Ball 2017). Work overload is a key job demand that incorporates time pressure and staffing and the amount of work required within the time available, rather than the quality of work (Broetje, Jenny, and Bauer 2020). These stimuli initiate coping strategies which appear to require emotional resources including, drawing on emotional reserves, and impact on job satisfaction and intention to leave.

The primary appraisal of workload may lead the nurse to consider their workload to be unsafe, this reflects attribution of meaning; being unsafe, to the situation when they are in an environment with staffing perceived as inadequate. The harm they may be considering is their resilience to provide the standard of care they aspire to, or concern that they may make an error, or that a patient may be harmed by the situation. They may also be considering the impact on their professional registration and their livelihood should error or harm occur. This led to the following hypothesis:

Hypothesis 1

There is a negative association between workload and job satisfaction.

2.2.2. Quality of Work

Quality of work may relate to the nurse's perception of the quality of care able to be provided, the ability to provide the care required or the outcomes of the care, for example, adverse events. The relationship between nurse staffing, the quality of the practice environment and quality of care has been extensively examined (Aiken, Clarke, and Sloane 2002; Ausserhofer et al. 2014; Duffield et al. 2011; Spence Laschinger and Leiter 2006). Relationships have also been established with nurse reported quality of care (Aiken, Clarke, and Sloane 2002; Kim et al. 2009; Spence Laschinger and Leiter 2006), nurse reported nursing care left undone (missed care) (Ausserhofer et al. 2014) and nurse reported frequency of adverse events (Spence Laschinger and Leiter 2006). All of these influence the nurse's perception of the quality of work they can provide.

Leadership and professional resources that support the quality of care are identified as key resources influencing the quality of work (Broetje, Jenny, and Bauer 2020). Job resources enable achievement of goals, reducing job demands and stimulate personal growth and development (Demerouti et al. 2001) and are also characteristics of the practice environment. Perceptions of unit care quality (Lake 2002) is often used to understand quality of care. Nurse staffing was commonly associated with quality of care (Aiken, Clarke, and Sloane 2002; Ausserhofer et al. 2014; Duffield et al. 2011; Spence Laschinger and Leiter 2006). Quality of care was predicted by staffing (Lake 2002) and indirectly predicted by job satisfaction through burnout (Liu and Aungsuroch 2018). Workload predicted nurse assessed quality of care (Van Bogaert et al. 2012).

The outcome of the nurse's primary appraisal of their workload may be that it is manageable or unsafe. They may take an emotion focused approach to the workload problem and seek emotional support from colleagues, re‐frame their willingness to support the emotional and relational needs of patients, or their aspirations to provide high quality care. Alternatively, a problem focused approach may result in receiving help from colleagues or other units to provide the quality of work desired. This led to the following hypothesis:

Hypothesis 2

There is a negative association between workload and quality of work.

2.2.3. Missed Care

Missed care can be defined as omissions or delays in nursing care caused by reduced capacity in the nursing team (Griffiths et al. 2018a, p. 25). Bruyneel et al. (2015) demonstrated a relationship between staffing adequacy, missed care and patient satisfaction. Labour resources (96.3%), teamwork (91%), material resources (90%) and communication (85.3%) were the reasons identified for commonly missing care (Haftu et al. 2019). Missed care was significantly related to perceived staffing levels (Jones, Hamilton, and Murry 2015; Kalisch, Tschannen, and Lee 2012; Tubbs‐Cooley et al. 2019), care quality (Jones, Hamilton, and Murry 2015; Kalisch, Tschannen, and Lee 2012; Kvist et al. 2012), and thoroughness and timeliness of care (Tubbs‐Cooley et al. 2019). The nurse therefore may take a problem focused approach to the workload problem. They may consider their patient needs, seek assistance, in the form of more staff or ward help, or prioritise, re‐prioritise or ration care. This led to the following hypotheses:

Hypothesis 3

There is a negative association between quality of work and missed care.

Hypothesis 4

There is a positive association between workload and missed care.

2.2.4. Job Satisfaction

Job dissatisfaction is considered an outcome of unfinished care (Jones, Hamilton, and Murry 2015) and is a predictor of intent to leave (Jiang et al. 2019; Nowrouzi‐Kia and Fox 2020; Sojane, Klopper, and Coetzee 2016; Tschannen, Kalisch, and Lee 2010). Job satisfaction was associated with the nurses work environment which was in turn related to intention to stay (Al‐Hamdan, Manojlovich, and Tanima 2017). Han, Trinkoff, and Gurses (2015) found that dissatisfaction was related to higher psychological demands and lower autonomy while intention to stay was related to peer support and autonomy. Job satisfaction, was negatively associated with turnover intention and had a mediating effect on the relationship between job characteristics and turnover intention at individual level (Portoghese et al. 2015). In Japan, intention to stay was positively associated with work engagement, getting appropriate support from nurse managers and perceived quality of care process (Eltaybani et al. 2018). Job satisfaction was also noted to be related to intention to leave the job and profession (Sasso et al. 2019). Smith, Rogowski, and Lake (2019) showed that each one‐unit increase in missed care was associated with a 0.26 decrease in job enjoyment and increased odds of intention to leave by 29%.

Job satisfaction indirectly affected quality of nursing through burnout (Liu and Aungsuroch 2018). Kvist et al. (2012) demonstrated the positive relationship between job satisfaction and quality of care as those who considered their working units to provide an excellent quality of care reported the highest job satisfaction in every sub‐area. Intention to leave the institution was shown to have a significant association with commonly missing care (Haftu et al. 2019). As workload demands influencing the ability to provide quality care and meet all the care requirements predict job satisfaction, the nurse may take an emotional approach to the workload problem (Winters and Neville 2012). They may re‐evaluate their job satisfaction or even their career choice. This may be accentuated if their re‐appraisal of the situation leads them to conclude that their coping strategies are unsuccessful. This led to the following hypotheses:

Hypothesis 5

There is a positive association between quality of work and job satisfaction.

Hypothesis 6

There is a negative association between missed care and job satisfaction.

While much is known about the many factors that influence nurses' experience of work we have not captured them in practice as a ‘shift in action’. This is significant because nurse leaders need to understand the patterns to be identified between particular shift configurations and impacts on nurses in their organisation.

3. Methods

3.1. Design

This is a cross‐sectional self‐report survey of the experiences of nurses at the end of their shift. All items were drawn from validated scales (see File S1).

3.2. Study Setting and Sampling

A total of 1111 nurses and health care assistants employed in Adult Medical Services including ED, ICU and stroke wards in two acute district hospitals in NZ. Participants were invited to participate via a poster invitation displayed with a QR code in the clinical area. Handover discussions directed staff to the poster and encouraged participation. A sample of at least 200 participants is recommended as sufficient for structural equation modelling (Kline 2016).

3.3. Inclusion and Exclusion Criteria

Staff were eligible to participate if they were nurses and health care assistants working at the time in the services included. Staff who were not nurses and health care assistants working in these areas were excluded.

3.4. Instrument, Validity and Reliability

The EOSS survey comprised 18‐items of which 11‐items were drawn from three validated tools identified through the literature and seven were participant characteristics (see File S2). The measures are explained in detail in Section 3.8.

3.5. Data Collection

Participation was voluntary as staff had to respond to the poster, launch their camera at a QR code on the poster, and open and complete the survey. The survey was available for participants to complete online during May 2022.

3.6. Data Analysis

Firstly, we performed descriptive analyses. Using IBM SPSS v.27, we undertook an exploratory factor analysis (EFA) of the nursing work items using principal components with varimax rotation. To check for validity and discriminant reliability of the scales, we computed Cronbach's alpha and average variance estimates (AVEs) for latent variables. Convergent validity is demonstrated by the AVE all > 0.5, thus meeting the minimum cut‐off established in the literature (Hu and Bentler 1999). Discriminant validity is demonstrated where the square root of the AVE is greater than any of the inter‐factor correlations. We also conducted a Fornell and Larcker (1981) AVE test for these latent variables.

We conducted a confirmatory factor analysis (CFA) on those scales to determine goodness of fit. Prior to conducting hypotheses testing, we standardised the items as they were measured with different rating scales (such as 4‐ and 5‐point Likert scale and percentages). We then used the measurement model to compute the latent variables for model testing. Following the two‐step approach by Anderson and Gerbing (1988), a second‐order latent variable for workload and quality of work was then imputed for hypotheses testing in IBM SPSS v27.

3.7. Ethical Statement

Approval for the study was obtained from the Auckland Health Research Ethics Committee (AHREC) (12 October 2022) and locality approval was granted from the organisation involved in the study (29 November 2022).

3.8. Measures

We undertook an exploratory factor analysis (EFA) of nursing work items from NASA Task Load Index (TLX) Tubbs‐Cooley et al. (2018), Hamilton et al. (2017) and Aiken, Clarke, and Sloane (2002). EFA resulted in two factors: workload and quality of work (see below). CFA supported the two‐factor structure from the EFA (as discussed in the next section). Independent and dependent variables were continuous variables while control variables were categorical variables.

3.8.1. Workload

Exploratory factor analysis resulted in a three‐item workload factor which was consistent with the NASA Task Load Index (TLX) (Tubbs‐Cooley et al. 2018). The fourth item, success in accomplishing goals during the shift, became part of the second factor in the EFA. Participants were asked to rate the degree to which their workload was mentally and physically demanding, and how hurried they were in performing their job. The items used a 20‐point Likert scale where ‘1’ is the least mentally demanding and ‘20’ the most mentally demanding.

3.8.2. Quality of Work

The second factor from the EFA represents quality of work. Quality of work is measured using five items. One item measured perceived quality of care on the shift (Aiken, Clarke, and Sloane 2002; Parr, Teo, and Koziol‐McLain 2021) and three items from Hamilton et al. (2017) measured thoroughness and timeliness of care and another asked participants to rate the quality of care on their unit. One item from the NASA Task Load Index (TLX) (Tubbs‐Cooley et al. 2018), success in accomplishing goals during the shift, loaded on the quality of work factor in the EFA. Participants rated the timeliness and thoroughness of the care provided using 5‐point scales (1) very good to (5) very poor. Participants rated the degree to which they were able to accomplish their goals during the shift using a 20‐point Likert scale where ‘1’ is the least successful and ‘20’ the most successful. Participants were rated their perceptions of the quality of care on the shift and unit using a 4‐point Likert scale from (1) excellent to (4) poor. These items were reverse coded so that high value represents high value of quality of work.

3.8.3. Missed Care

We used the Hamilton et al. (2017) single item global estimate to measure missed care in nursing. Participants were asked to rate the percent of missed care in their unit captured by the statement To the best of your knowledge what percent of nursing or midwifery care is missed (at least occasionally) by nursing or midwifery staff in your ward/unit? Please provide your estimate as a number from 1 to 100. High value represents high level of missed care.

3.8.4. Job Satisfaction

We used the Hamilton et al. (2017) single item indicator to measure job satisfaction as it was developed as part of the SGE inventory associated with missed care in nursing. A global single item scale for measuring job satisfaction has been established in the business and psychology literature and has been shown to correlate with those measuring facets of job satisfaction (Bowling and Zelazny 2022; Nagy 2002). Participants rated their job satisfaction using a 4‐point Likert Scale (1) Very satisfied to (4) Very dissatisfied. The question was ‘How satisfied are you in your current position?’

3.8.5. Control Variables

Years in practice and shift type were used as control variables reflecting findings relating to workload (Lawless and Bowden 2013), quality of care (Sochalski 2001), intention to leave (Burmeister et al. 2019), variance of missed care by shift type (Haftu et al. 2019). Three categorical control variables were used included: age, years of practice and shift type (‘1’ AM, ‘2’ other day, ‘3’ 12‐h day, ‘4’ PM, ‘5’ night, ‘6’ 12‐h night, ‘7’ other night). As these were categorical variables, these were used in the bivariate correlation analysis.

3.9. Common Method Bias

Common method bias is a concern when combining self‐report variables. We used the Harman's one factor post hoc test to check for common method bias (Podsakoff et al. 2003). The result of the unrotated factor analysis produced a two‐factor solution where the largest single factor with > 1.0 eigenvalues was 49%. The result provided some assurance that common method variance was of no major concern. As concluded by a recent review of the empirical evidence of the effect of CMV in the last 10 years by Bozionelos and Simmering (2022, p. 194) noted that ‘… the probability of significant distortion of estimates because of CMV is very limited’.

3.10. Validity, Reliability and Rigour

Results of the checks for validity and reliability of the scales are presented in Table 1. CFA produced a goodness of fit indices (χ2 = 34.971, df = 18, CFI =0.988, TLI = 0.981, RMSEA = 0.059, SRMR = 0.039) which suggest a good model fit, consistent with the cut‐offs recommended by Hu and Bentler (1999).

TABLE 1.

Construct reliability and validity.

α AVE MSV MaxR (H) Quality of work Workload
Quality of work 0.904 0.659 0.250 0.928 0.812
Workload 0.879 0.709 0.250 0.879 −0.500 (p = 0.001) 0.842
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Note: Bold numbers are square root of AVEs.

Abbreviations: Alpha (α), Cronbach's alpha; AVE, average variance extracted; MaxR, maximum reliability; MSV, maximum shared variance.

4. Results

A total of 265 complete responses remained for further analyses after 22 cases were removed due to outliers and where the age, shift and years in practice were missing, yielding a 24% response rate. The mode and median time to complete the survey was 3 min. Frequency distribution and sample characteristics are reported in Table 2. Most participants were registered nurses (90.9%), worked in a medical speciality area (33.21%), were Asian (46%), aged between the ages of 25 and 34 (41.5%), had 6–10 years in practice (26.8%) and had worked either an AM or 12‐h day shift (50.5%).

TABLE 2.

Frequency distribution and sample characteristics.

Demographics Frequency Percent
Role
Nurse 241 90.9
HCA 23 8.7
Speciality
Adult rehabilitation service 4 1.51
Emergency medicine—ED 73 27.55
General medicine 34 12.83
General surgery 1 0.38
ICU 21 7.92
Medical speciality 88 33.21
Paediatric medicine 5 1.89
Stroke 22 8.3
Other 15 5.66
Ethnicity
European 64 24.2
Maaori 18 6.8
Pacific peoples 30 11.3
Asian 122 46.0
MELAA (Middle Eastern/Latin American/African) 1 0.4
Other ethnicity 27 10.2
Not elsewhere included 2 0.8
Age
24 and under 26 9.8
25–34 110 41.5
35–44 79 29.8
45–54 30 11.3
55–64 17 6.4
65 and over 3 1.1
Years in practice
New graduate 22 8.3
2–5 69 26.0
6–10 71 26.8
11–15 61 23.0
16–20 15 5.7
21–25 13 4.9
26–30 5 1.9
31–35 6 2.3
36–40 3 1.1
Shift last worked (shift type)
AM 887 32.8
PM 66 24.9
Other day 8 3.0
Night 28 10.6
12‐h day 47 17.7
12‐h night 28 10.6
Other night 1 0.4
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Note: N = 265. Descriptive statistics and intercorrelations are reported in Table 3. Bivariate correlations showed the variables were statistically associated in the direction as predicted by the hypotheses. The mean for workload was 14.83 (SD 3.82), quality of work was 2.97 (SD 0.89), missed care was 37.89 (SD 22.25) and job satisfaction was 2.60 (SD 0.73). There was only one statistically significant correlation between age and years in practice where older respondents had more years of clinical experience.

TABLE 3.

Descriptive statistics and inter‐correlations.

1 2 3 4 5 6 7
1. Age 1.000
2. Shift −0.139, p = 0.023 1.000
3. Years in practice 0.615, p ≤ 0.001 0.010, p = 0.866 1.000
4. Workload −0.096, p = 0.113 0.059, p = 0.340 0.023, p = 0.706 1.000
5. Quality of work 0.106, p = 0.081 −0.282, p ≤ 0.001 −0.083, p = 0.170 −0.533, p ≤ 0.001 1.000
6. Missed care −0.064, p = 0.295 −0.017, p = 0.005 −0.010, p = 0.110 0.376, p ≤ 0.001 −0.524, p ≤ 0.001 1.000
7. Job satisfaction 0.105, p = 0.083 −0.306, p ≤ 0.001 −0.002, p = 0.969 −0.429, p ≤ 0.001 0.626, p ≤ 0.001 −0.427, p ≤ 0.001 1.000
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Note: N = 265. Results of the multiple regression are reported in Table 4. All of the hypotheses were supported. There was a significant association between workload and quality of work (β = −0.533, p < 0.001), missed care (β = 0.135, p = 0.027) and job satisfaction (β = −0.117, p = 0.043). There was a negative, significant association between quality of work and missed care (β = −0.453, p < 0.001). There was a positive, significant association between quality of work and job satisfaction (β = 0.500, p < 0.001) and the association between missed care and job satisfaction was negative (β = −0.117, p = 0.036). The analysis shows 41.5% of the variation in job satisfaction could be explained by the independent variables in the hypothesised model.

TABLE 4.

Results of multiple regression analyses.

Quality of work Missed care Job satisfaction
Std. coeff p 95% LLCI 95% ULCI Std. coeff p 95% LLCI 95% ULCI Std. coeff p 95% LLCI 95% ULCI
Workload −0.533 < 0.001 −0.148 −0.101 0.135 0.027 0.091 1.479 −0.117 0.036 −0.043 −0.002
Quality of work −0.453 < 0.001 −14.235 −8.298 0.500 < 0.001 0.310 0.503
Missed care −0.121 0.029 −0.007 −0.001
R 2 0.284 0.288 0.415
F‐statistics 107.742 54.808 63.931
p‐value < 0.001 < 0.001 < 0.001
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Note: N = 265.

Abbreviations: LLCI, lower level confidence limit; Std. coeff, standardised coefficients; ULCI, upper level confidence limit.

5. Discussion

The aim of this research was to explore and validate an EOSS which had a low response burden, practical application and generated evidence of related associations between workload, quality of work and patient care, missed care and job satisfaction.

The global nursing shortage has a daily impact on the experience and well‐being of nurses (Holland et al. 2019; World Health Organization 2020). Nurses, through their practice and prioritisation decisions influence quality of care and missed care (Jones, Hamilton, and Murry 2015; Kalisch, Tschannen, and Lee 2012). Nurses experience lowered satisfaction if conditions are such that they are forced to lower quality (including missing care) (Kalisch, Tschannen, and Lee 2012; Tschannen, Kalisch, and Lee 2010). Coping strategies which were once effective, may become less so over time (Biggs, Brough, and Drummond 2017). Repeated negative experiences and the subsequent shift towards increased missed care, particularly of relational components of care may lead nurses to question their role and profession (Jones, Hamilton, and Murry 2015).

Previous research has investigated relationships between workload and job satisfaction (Han, Trinkoff, and Gurses 2015) workload and missed care (Ball 2017), missed care and job satisfaction (Smith, Rogowski, and Lake 2019) and missed care and intention to leave (Sasso et al. 2019). Our findings suggest there are significant relationships between workload, quality of work, missed care and job satisfaction. Workload, quality of work and missed care all had significant relationships with job satisfaction in the expected directions. The bivariate correlations support the hypotheses and associations between the EOSS factors and global items.

This research brings all these components together for the first time. The EOSS is unique as it comprises two factors; workload and quality of work, and two global item measures; missed care and job satisfaction. The EOSS demonstrated construct reliability and validity. Workload is a modifiable job demand, which not only constitutes patient acuity demands, but also environmental factors such as documentation, medication administration processes (Teoh et al. 2023). Staffing in relation to demand is the largest contributor to the ‘size’ of nursing workload. Excessive workload is associated with a number of negative outcomes. The EOSS enables diagnosis of the issue. If negative factors are observed, hospital leaders are directed to action to ameliorate workload. If negative factors are not seen, this gives hospital leaders confidence that workload is ‘about right’. Capturing the factors as associated with ‘shifts in action’ allows, over time, patterns of staffing configuration to be observed and modified.

The development of the EOSS, extends the concept of the Fagerström and Rainio (1999) tool. The transactional model of stress provides a helpful lens to consider the meaning nurses apply to high workload situations and illuminates previous findings where workload demands are associated with retention of nurses (Clausen, Tufte, and Borg 2014). The nurse's experiences of and meaning attributed to stressful events such as excessive workload, are subjective, individual and influenced by their own experience, frequency of exposure to and the success of their coping strategies (Biggs, Brough, and Drummond 2017).

Nurses will have little incentive to use an EOSS unless they have confidence that safe staffing and workload are attended to. To be effective, commitment to heeding and acting on the outcomes is imperative. Our goal as nursing leaders is to ensure the care we and our profession provide is of the highest quality. We need to take every action to address high workload to reduce the risk that fundamental care is not sacrificed, job satisfaction is improved and nurses remain in the profession. The EOSS is a valid, reliable and quick practical survey that nursing leaders can adopt to measure, understand and address broader constructs surrounding the experience of nurses and their consequences to improve retention. Further research is necessary to investigate the relationship of the EOSS to available care hours and validate the tool for use by other professions such as midwifery and allied health professions.

5.1. Limitations

Data were collected at one point in time, which could affect the generalisability of the findings and potentially be affected by common method bias (Podsakoff, MacKenzie, and Podsakoff 2012). Consistent with the literature, we conducted two post hoc statistical remedies—Harman's single factor test and common factor latent model to check for common method bias. Both of these tests indicated the potential of common method bias was not evident as the test results were less than the cut‐offs for common method bias in the data. Future studies could address this limitation by collecting objective performance data similar to those in the literature (e.g., Parr, Teo, and Koziol‐McLain 2021). Data were two hospitals in NZ. To improve generalisable findings, we could test the result across other professions and those from other countries.

6. Conclusion

This study adds to the existing body of knowledge related to associations between workload, quality of work, missed care and job satisfaction for nurses. It also provides for the first time, a validated tool to measure the associations found in the EOSS with a low response burden. Importantly, the EOSS captures data from a ‘shift in action’. This will give nurse leaders a reliable, practical, consistent, applied tool that will better enable associations to be observed between resource configuration, workload and critical impacts on nursing and patient care.

Author Contributions

J.M.P., J.S. and S.T.T.T. made substantial contributions to conceptualisation of ideas, research goals and aims. J.M.P. and S.T.T.T. developed the design and methodology and model, undertook data curation and validation of the data and results. S.T.T.T. undertook the formal analysis. J.M.P. acquired the data for investigation, wrote the original draft. J.M.P., J.S., J.L. and S.T.T.T. were involved reviewing and editing it; gave final approval of the version to be published; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

File S1. The instrument validity and reliability for 11‐items which were drawn from three validated tools identified through the literature review.

JOCN-34-2324-s001.docx (30.9KB, docx)

File S2. The end of shift survey comprising 18‐items of which seven were participant characteristics. The survey was available for participants to complete online during May 2022. Participants were invited to participate via a poster invitation displayed in their units.

JOCN-34-2324-s003.docx (31.7KB, docx)

Data S1. STROBE statement—Checklist of items that should be included in reports of cross‐sectional studies.

JOCN-34-2324-s002.docx (33.1KB, docx)

Acknowledgements

The authors express their thanks to colleagues Chris Kerr, from Capital and Coast DHB, Kate Weston, Jinny Willis and Maree Jones of the New Zealand Nurses Organisation, and Caroline Conroy from MERAS for their help and advice in this project. There is a statistician on the author team Professor Stephen Teo. Open access publishing facilitated by The University of Auckland, as part of the Wiley ‐ The University of Auckland agreement via the Council of Australian University Librarians.

Funding: The authors received no specific funding for this work.

Data Availability Statement

All data utilised in the submitted manuscript have been lawfully acquired in accordance with The Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from Their Utilization to the Convention on Biological Diversity.

References

  1. Aiken, L. H. , Clarke S. P., and Sloane D. M.. 2002. “Hospital Staffing, Organization, and Quality of Care: Cross‐National Findings.” International Journal for Quality in Health Care 14, no. 1: 5–13. 10.1093/intqhc/14.1.5. [DOI] [PubMed] [Google Scholar]
  2. Aiken, L. H. , Clarke S. P., Sloane D. M., Lake E. T., and Cheney T.. 2008. “Effects of Hospital Care Environment on Patient Mortality and Nurse Outcomes.” Journal of Nursing Administration 38, no. 5: 223–229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Al‐Hamdan, Z. , Manojlovich M., and Tanima B.. 2017. “Jordanian Nursing Work Environments, Intent to Stay, and Job Satisfaction.” Journal of Nursing Scholarship 49, no. 1: 103–110. [DOI] [PubMed] [Google Scholar]
  4. American Hospital Association . 2023. “Health Care Workforce Scan.” Executive Summary. https://www.aha.org/system/files/media/file/2022/11/2023_AHA_Health_Care_Workforce_Scan_Executive_Summary.pdf.
  5. Anderson, J. C. , and Gerbing D. W.. 1988. “Structural Equation Modeling in Practice: A Review and Recommended Two‐Step Approach.” Psychological Bulletin 103, no. 3: 411–423. [Google Scholar]
  6. Ausserhofer, D. , Zander B., Busse R., et al. 2014. “Prevalence, Patterns and Predictors of Nursing Care Left Undone in European Hospitals: Results From the Multicountry Cross‐Sectional RN4CAST Study.” BMJ Quality and Safety 23, no. 2: 126–135. 10.1136/bmjqs-2013-002318. [DOI] [PubMed] [Google Scholar]
  7. Ball, J. E. 2017. Nurse Staffing, Care Left Undone and Patient Mortality in Acute Hospitals. Solna, Sweden: Karolinska Institutet. [Google Scholar]
  8. Bazazan, A. , Dianat I., Bahrampour S., et al. 2019. “Association of Musculoskeletal Disorders and Workload With Work Schedule and Job Satisfaction Among Emergency Nurses.” International Emergency Nursing 44: 8–13. [DOI] [PubMed] [Google Scholar]
  9. Biggs, A. , Brough P., and Drummond S.. 2017. “Lazarus and Folkman's Psychological Stress and Coping Theory.” In Handbook of Stress and Health: A Guide to Research and Practice, edited by Cooper Cary L., Campbell Quick James, 349–364. West Sussex, UK: John Wiley & Sons Ltd. [Google Scholar]
  10. Bowling, N. , and Zelazny L.. 2022. “Measuring General Job Satisfaction: Which Is More Construct Valid—Global Scales or Facet‐Composite Scales?” Journal of Business and Psychology 37, no. 1: 91–105. 10.1007/s10869-021-09739-2. [DOI] [Google Scholar]
  11. Bozionelos, N. , and Simmering M. J.. 2022. “Methodological Threat or Myth? Evaluating the Current State of Evidence on Common Method Variance in Human Resource Management Research.” Human Resource Management Journal 32, no. 1: 194–215. 10.1111/1748-8583.12398. [DOI] [Google Scholar]
  12. Broetje, S. , Jenny G. J., and Bauer G. F.. 2020. “The Key Job Demands and Resources of Nursing Staff: An Integrative Review of Reviews.” Frontiers in Psychology 11: 84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Bruyneel, L. , Li B., Ausserhofer D., et al. 2015. “Organization of Hospital Nursing, Provision of Nursing Care, and Patient Experiences With Care in Europe.” Medical Care Research and Review 72, no. 6: 643–664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Buchan, J. , and Catton H.. 2020. “COVID 19 and the International Supply of Nurses.”
  15. Burmeister, E. A. , Kalisch B. J., Xie B., et al. 2019. “Determinants of Nurse Absenteeism and Intent to Leave: An International Study.” Journal of Nursing Management 27, no. 1: 143–153. [DOI] [PubMed] [Google Scholar]
  16. Clausen, T. , Tufte P., and Borg V.. 2014. “Why Are They Leaving? Causes of Actual Turnover in the Danish Eldercare Services.” Journal of Nursing Management 22, no. 5: 583–592. [DOI] [PubMed] [Google Scholar]
  17. Crossan, D. 2006. “Report of the Safe Staffing/Healthy Workplaces Committee of Inquiry.” http://www.dhbsharedservices.health.nz/Site/Future_Workforce/SSHWU/Overview/Agenda.aspx.
  18. Demerouti, E. , Bakker A. B., Nachreiner F., and Schaufeli W. B.. 2001. “The Job Demands‐Resources Model of Burnout.” Journal of Applied Psychology 86, no. 3: 499–512. 10.1037/0021-9010.86.3.499. [DOI] [PubMed] [Google Scholar]
  19. Dewe, P. , and Cooper G. L.. 2007. “Coping Research and Measurement in the Context of Work Related Stress.” International Review of Industrial and Organizational Psychology 2007: 141–192. [Google Scholar]
  20. Duffield, C. , Diers D., O'Brien‐Pallas L., et al. 2011. “Nursing Staffing, Nursing Workload, the Work Environment and Patient Outcomes.” Applied Nursing Research 24, no. 4: 244–255. 10.1016/j.apnr.2009.12.004. [DOI] [PubMed] [Google Scholar]
  21. Eltaybani, S. , Noguchi‐Watanabe M., Igarashi A., Saito Y., and Yamamoto‐Mitani N.. 2018. “Factors Related to Intention to Stay in the Current Workplace Among Long‐Term Care Nurses: A Nationwide Survey.” International Journal of Nursing Studies 80: 118–127. [DOI] [PubMed] [Google Scholar]
  22. Fagerström, L. , and Rainio A.‐K.. 1999. “Professional Assessment of Optimal Nursing Care Intensity Level: A New Method of Assessing Personnel Resources for Nursing Care.” Journal of Clinical Nursing 8, no. 4: 369–379. [DOI] [PubMed] [Google Scholar]
  23. Feo, R. , Conroy T., Jangland E., et al. 2017. “Towards a Standardised Definition for Fundamental Care: A Modified Delphi Study.” Journal of Clinical Nursing 27, no. 11–12: 2285–2299. [DOI] [PubMed] [Google Scholar]
  24. Fornell, C. , and Larcker D. F.. 1981. “Evaluating Structural Equation Models With Unobservable Variables and Measurement Error.” Journal of Marketing Research 18, no. 1: 39–50. 10.2307/3151312. [DOI] [Google Scholar]
  25. Griffiths, P. , Ball J., Bloor K., et al. 2018a. “Nurse Staffing Levels, Missed Vital Signs and Mortality in Hospitals: Retrospective Longitudinal Observational Study.” Health Services and Delivery Research 6, no. 38: 1–120. [PubMed] [Google Scholar]
  26. Griffiths, P. , Recio‐Saucedo A., Dall'Ora C., et al. 2018b. “The Association Between Nurse Staffing and Omissions in Nursing Care: A Systematic Review.” Journal of Advanced Nursing 74, no. 7: 1474–1487. 10.1111/jan.13564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Haftu, M. , Girmay A., Gebremeskel M., Aregawi G., Gebregziabher D., and Robles C.. 2019. “Commonly Missed Nursing Cares in the Obstetrics and Gynecologic Wards of Tigray General Hospitals, Northern Ethiopia.” PLoS One 14, no. 12: e0225814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hamilton, P. , Willis E., Jones T., et al. 2017. “Evaluating the Performance of a Single‐Item, Global, Estimate of Missed Nursing Care.” Journal of Nursing Measurement 25, no. 1: 121–141. [DOI] [PubMed] [Google Scholar]
  29. Han, K. , Trinkoff A. M., and Gurses A. P.. 2015. “Work‐Related Factors, Job Satisfaction and Intent to Leave the Current Job Among United States Nurses.” Journal of Clinical Nursing 24, no. 21–22: 3224–3232. 10.1111/jocn.12987. [DOI] [PubMed] [Google Scholar]
  30. Healy, C. M. , and McKay M. F.. 2000. “Nursing Stress: The Effects of Coping Strategies and Job Satisfaction in a Sample of Australian Nurses.” Journal of Advanced Nursing 31, no. 3: 681–688. [DOI] [PubMed] [Google Scholar]
  31. Holland, P. , Tham T. L., Sheehan C., and Cooper B.. 2019. “The Impact of Perceived Workload on Nurse Satisfaction With Work‐Life Balance and Intention to Leave the Occupation.” Applied Nursing Research 49: 70–76. [DOI] [PubMed] [Google Scholar]
  32. Hu, L. , and Bentler P. M.. 1999. “Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives.” Structural Equation Modeling: A Multidisciplinary Journal 6, no. 1: 1–55. [Google Scholar]
  33. Jiang, F. , Zhou H., Rakofsky J., et al. 2019. “Intention to Leave and Associated Factors Among Psychiatric Nurses in China: A Nationwide Cross‐Sectional Study.” International Journal of Nursing Studies 94: 159–165. [DOI] [PubMed] [Google Scholar]
  34. Jones, T. L. , Hamilton P., and Murry N.. 2015. “Unfinished Nursing Care, Missed Care, and Implicitly Rationed Care: State of the Science Review.” International Journal of Nursing Studies 52, no. 6: 1121–1137. [DOI] [PubMed] [Google Scholar]
  35. Kalisch, B. J. , Tschannen D., and Lee K. H.. 2012. “Missed Nursing Care, Staffing, and Patient Falls.” Journal of Nursing Care Quality 27, no. 1: 6–12. [DOI] [PubMed] [Google Scholar]
  36. Kim, H. , Capezuti E., Boltz M., and Fairchild S.. 2009. “The Nursing Practice Environment and Nurse‐Perceived Quality of Geriatric Care in Hospitals.” Western Journal of Nursing Research 31, no. 4: 480–495. 10.1177/0193945909331429. [DOI] [PubMed] [Google Scholar]
  37. Kline, R. B. 2016. Principles and Practice of Structural Equation Modeling. 4th ed. New York, NY: Guilford Press. [Google Scholar]
  38. Kutney‐Lee, A. , Germack H., Hatfield L., et al. 2016. “Nurse Engagement in Shared Governance and Patient and Nurse Outcomes.” Journal of Nursing Administration 46, no. 11: 605–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kvist, T. , Mäntynen R., Partanen P., Turunen H., Miettinen M., and Vehviläinen‐Julkunen K.. 2012. “The Job Satisfaction of Finnish Nursing Staff: The Development of a Job Satisfaction Scale and Survey Results.” Nursing Research and Practice 2012: 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lake, E. T. 2002. “Development of the Practice Environment Scale of the Nursing Work Index.” Research in Nursing and Health 25, no. 3: 176–188. [DOI] [PubMed] [Google Scholar]
  41. Lawless, J. , and Bowden N.. 2013. “Safe Staffing Healthy Workplaces Survey Analysis. Work Effort: A Sentinel Metric for Assessing Service Wellbeing?”
  42. Lazarus, R. S. , and Folkman S.. 1984. Stress, Appraisal, and Coping. Berlin, Germany: Springer. [Google Scholar]
  43. Leveck, M. L. , and Jones C. B.. 1996. “The Nursing Practice Environment, Staff Retention, and Quality of Care.” Research in Nursing & Health 19, no. 4: 331–343. [DOI] [PubMed] [Google Scholar]
  44. Liu, Y. , and Aungsuroch Y.. 2018. “Factors Influencing Nurse‐Assessed Quality Nursing Care: A Cross‐Sectional Study in Hospitals.” Journal of Advanced Nursing 74, no. 4: 935–945. 10.1111/jan.13507. [DOI] [PubMed] [Google Scholar]
  45. Lorente, L. , Vera M., and Peiró T.. 2021. “Nurses Stressors and Psychological Distress During the COVID‐19 Pandemic: The Mediating Role of Coping and Resilience.” Journal of Advanced Nursing 77, no. 3: 1335–1344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. McVicar, A. 2016. “Scoping the Common Antecedents of Job Stress and Job Satisfaction for Nurses (2000–2013) Using the Job Demands–Resources Model of Stress.” Journal of Nursing Management 24, no. 2: E112–E136. [DOI] [PubMed] [Google Scholar]
  47. Moloney, W. , Boxall P., Parsons M., and Cheung G.. 2018. “Factors Predicting Registered Nurses' Intentions to Leave Their Organization and Profession: A Job Demands‐Resources Framework.” Journal of Advanced Nursing 74, no. 4: 864–875. [DOI] [PubMed] [Google Scholar]
  48. Nagy, M. S. 2002. “Using a Single‐Item Approach to Measure Facet Job Satisfaction.” Journal of Occupational and Organizational Psychology 75, no. 1: 77–86. 10.1348/096317902167658. [DOI] [Google Scholar]
  49. National Institute for Health and Care Excellence . 2014a. “Safe Staffing for Nursing in Adult Inpatient Wards in Acute Hospitals.” In Safe Staffing Guideline 1, 48. Manchester, UK: National Institute for Health and Care Excellence. [Google Scholar]
  50. National Institute for Health and Care Excellence . 2014b. “Safe Staffing: The New Zealand Public Health Sector Experience.” In Expert Paper 1: Expert Testimony Presented to the Safe Staffing Advisory Committee, edited by Lawless J.. Manchester, UK: National Institute for Health and Care Excellence. [Google Scholar]
  51. Nowrouzi‐Kia, B. , and Fox M. T.. 2020. “Factors Associated With Intent to Leave in Registered Nurses Working in Acute Care Hospitals: A Cross‐Sectional Study in Ontario, Canada.” Workplace Health & Safety 68, no. 3: 121–128. [DOI] [PubMed] [Google Scholar]
  52. Parr, J. , Teo S., and Koziol‐McLain J.. 2021. “A Quest for Quality Care: Exploration of a Model of Leadership Relationships, Work Engagement, and Patient Outcomes.” Journal of Advanced Nursing 77, no. 1: 207–220. 10.1111/jan.14583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Podsakoff, P. M. , MacKenzie S. B., Lee J.‐Y., and Podsakoff N. P.. 2003. “Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies.” Journal of Applied Psychology 88, no. 5: 879–903. [DOI] [PubMed] [Google Scholar]
  54. Podsakoff, P. M. , MacKenzie S. B., and Podsakoff N. P.. 2012. “Sources of Method Bias in Social Science Research and Recommendations on How to Control It.” Annual Review of Psychology 63: 539–569. [DOI] [PubMed] [Google Scholar]
  55. Portoghese, I. , Galletta M., Battistelli A., and Leiter M. P.. 2015. “A Multilevel Investigation on Nursing Turnover Intention: The Cross‐Level Role of Leader–Member Exchange.” Journal of Nursing Management 23, no. 6: 754–764. [DOI] [PubMed] [Google Scholar]
  56. Rauhala, A. , and Fagerström L.. 2004. “Determining Optimal Nursing Intensity: The RAFAELA Method.” Journal of Advanced Nursing 45, no. 4: 351–359. 10.1046/j.1365-2648.2003.02918.x. [DOI] [PubMed] [Google Scholar]
  57. Safe Staffing Healthy Workplaces . 2023. “Core Data Set—Mōhiohio Iho.” https://sshw.health.nz/ccdm‐programme/core‐data‐set/.
  58. Sasso, L. , Bagnasco A., Catania G., Zanini M., Aleo G., and Watson R.. 2019. “Push and Pull Factors of Nurses' Intention to Leave.” Journal of Nursing Management 27, no. 5: 946–954. 10.1111/jonm.12745. [DOI] [PubMed] [Google Scholar]
  59. Shembavnekar, N. , Buchan J., Bazeer N., et al. 2022. “REAL Centre Projections.”
  60. Shin, S. , Park J.‐H., and Bae S.‐H.. 2018. “Nurse Staffing and Nurse Outcomes: A Systematic Review and Meta‐Analysis.” Nursing Outlook 66, no. 3: 273–282. [DOI] [PubMed] [Google Scholar]
  61. Smith, J. G. , Rogowski J., and Lake E. T.. 2019. “Missed Care Relates to Nurse Job Enjoyment and Intention to Leave in Neonatal Intensive Care.” Journal of Nursing Management 28, no. 8: 1940–1947. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Sochalski, J. 2001. “Quality of Care, Nurse Staffing, and Patient Outcomes.” Policy, Politics & Nursing Practice 2, no. 1: 9–18. [Google Scholar]
  63. Sojane, J. S. , Klopper H. C., and Coetzee S. K.. 2016. “Leadership, Job Satisfaction and Intention to Leave Among Registered Nurses in the North West and Free State Provinces of South Africa.” Curationis 39, no. 1: e1–e10. 10.4102/curationis.v39i1.1585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Spence Laschinger, H. K. S. , and Leiter M.. 2006. “The Impact of Nursing Work Environments on Patient Safety Outcomes: The Mediating Role of Burnout/Engagement.” Journal of Nursing Administration 36, no. 5: 259–267. [DOI] [PubMed] [Google Scholar]
  65. Stimpfel, A. W. , Sloane D. M., and Aiken L. H.. 2012. “The Longer the Shifts for Hospital Nurses, the Higher the Levels of Burnout and Patient Dissatisfaction.” Health Affairs 31, no. 11: 2501–2509. 10.1377/hlthaff.2011.1377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Taghadosi, M. , and Nabizadeh Gharghozar Z.. 2019. “Intention to Leave of Nurses and Related Factors: A Systematic Review.” Scientific Journal of Nursing, Midwifery and Paramedical Faculty 4, no. 4: 1–14. [Google Scholar]
  67. Teoh, K. , Dhensa‐Kahlon R., Chistensen M., Frost F., Hatton E., and Neilsen K.. 2023. “Organisational Interventions to Support Staff Wellbeing; Case Studies and Learnings From the NHS.” https://www.som.org.uk/sites/som.org.uk/files/Organisational_Interventions_to_Support_Staff_Wellbeing_in_the_NHS.pdf.
  68. Tschannen, D. , Kalisch B. J., and Lee K. H.. 2010. “Missed Nursing Care: The Impact on Intention to Leave and Turnover.” Canadian Journal of Nursing Research Archive 42, no. 4: 22–39. [PubMed] [Google Scholar]
  69. Tubbs‐Cooley, H. L. , Mara C. A., Carle A. C., and Gurses A. P.. 2018. “The NASA Task Load Index as a Measure of Overall Workload Among Neonatal, Paediatric and Adult Intensive Care Nurses.” Intensive & Critical Care Nursing 46: 64–69. [DOI] [PubMed] [Google Scholar]
  70. Tubbs‐Cooley, H. L. , Mara C. A., Carle A. C., Mark B. A., and Pickler R. H.. 2019. “Association of Nurse Workload With Missed Nursing Care in the Neonatal Intensive Care Unit.” JAMA Pediatrics 173, no. 1: 44–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Van Bogaert, P. , Clarke S., Willems R., and Mondelaers M.. 2012. “Staff Engagement as a Target for Managing Work Environments in Psychiatric Hospitals: Implications for Workforce Stability and Quality of Care.” Journal of Clinical Nursing 22: 1717–1728. [DOI] [PubMed] [Google Scholar]
  72. Wargo‐Sugleris, M. , Robbins W., Lane C. J., and Phillips L. R.. 2018. “Job Satisfaction, Work Environment and Successful Ageing: Determinants of Delaying Retirement Among Acute Care Nurses.” Journal of Advanced Nursing 74, no. 4: 900–913. [DOI] [PubMed] [Google Scholar]
  73. Winters, R. , and Neville S.. 2012. “Registered Nurse Perspectives on Delayed or Missing Cares in a New Zealand Hospital.” Nursing Praxis in New Zealand 28, no. 1: 19–28. [PubMed] [Google Scholar]
  74. World Health Organization . 2020. State of the World's Nursing 2020: Investing in Education, Jobs and Leadership. Geneva, Switzerland: World Health Organization. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

File S1. The instrument validity and reliability for 11‐items which were drawn from three validated tools identified through the literature review.

JOCN-34-2324-s001.docx (30.9KB, docx)

File S2. The end of shift survey comprising 18‐items of which seven were participant characteristics. The survey was available for participants to complete online during May 2022. Participants were invited to participate via a poster invitation displayed in their units.

JOCN-34-2324-s003.docx (31.7KB, docx)

Data S1. STROBE statement—Checklist of items that should be included in reports of cross‐sectional studies.

JOCN-34-2324-s002.docx (33.1KB, docx)

Data Availability Statement

All data utilised in the submitted manuscript have been lawfully acquired in accordance with The Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from Their Utilization to the Convention on Biological Diversity.

Articles from Journal of Clinical Nursing are provided here courtesy of Wiley

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