Abstract
Aim
This study aimed to investigate scheduling in COVID‐19‐designated hospitals, including working hours, rest days, adverse nursing outcomes and their relationship.
Background
Hospitals are at the forefront of COVID‐19 prevention and control, and nurses are the main force on the frontline of the epidemic. Nursing shift is one of the most relevant and pressing issues for frontline nurses. However, there is a lack of national, large‐sample surveys on scheduling and adverse nursing outcomes in COVID‐19‐designated hospitals.
Methods
Using a cross‐sectional online survey, we used nurse‐reported data to measure the characteristics of the work setting, samples and work schedule. A descriptive analysis was performed to assess the shift status and adverse outcomes of designated hospitals and frontline nurses. Logistic regression analysis was performed to determine the association between them.
Results
Basic data were collected from 217 departments of 69 COVID‐19‐designated hospitals in 31 provinces in China. Nurses in the severe isolation ward worked mainly for 4 h per shift, whereas those in the fever clinic and observation ward worked mainly for 6–8 h. Half of the nurses had only 1 day of rest per week. Long working hours, lack of adequate rest time and overtime can seriously affect the quality and safety of nurses' work, resulting in adverse outcomes. Frontline nurses hope that scheduling guarantees the time to rest while ensuring fairness.
Conclusions
The current evidence showed that frontline nurses were faced with long working hours, insufficient rest and overtime, which has a negative impact on nurse satisfaction, physical and mental health and quality of care. The government, hospitals and administrators still face many problems to overcome in their nursing schedules.
Implications for Nursing Management
Governments and hospitals should take these factors, such as the work setting characteristics and demographic features of the frontline nurses, into account when selecting nurses to fight COVID‐19. Nurses have different working hours in different types of coronavirus unit; therefore, nursing managers should consider the working environment and nursing schedule needs, and in the future, we should pay attention to the fairness of nurses while ensuring their rest.
Keywords: adverse nurse outcomes, COVID‐19, designated hospital, nursing scheduling
1. BACKGROUND
The novel coronavirus disease 2019 (COVID‐19), which emerged in December 2019, remains endemic worldwide (Lancet, 2020). The World Health Organization (WHO, 2020) classifies the COVID‐19 pandemic as a major international public health emergency. The National Health Commission of the People's Republic of China (NHC, 2020b) stipulates that preventive and control measures for ‘Class A’ infectious diseases should be taken to control COVID‐19. According to the latest COVID‐19 statistics released by Johns Hopkins University on Monday (ss of 22 March at 6:00 pm ET), the total number of COVID‐19 deaths worldwide has exceeded 6 million, causing a severe disease burden across the globe.
Hospitals are at the forefront of COVID‐19 prevention and control, and nurses are the main force at the frontline of the epidemic (Liu, Luo, & Haase, 2020). To ensure the security of patients, nurses must provide continuous care services; therefore, a nursing schedule is inevitable (Bae & Fabry, 2014). Before the pandemic, nurses worked mainly on fixed shifts, most of which were 8 h per shift (day shift) and 2 days off per week (Kullberg et al., 2016). At this particular time of the epidemic, especially during the outbreak, nursing is facing a huge challenge (Liu, Wang, et al., 2020; Zhang, Jiang, et al., 2021). In contrast, because of wearing personal protective equipment (PPE) and physiological limits, frontline nurses have limited working hours (Gao et al., 2020). However, the conflict between surging demand from patients and shortage of nurses has increased sharply, resulting in serious overtime work and a serious lack of rest time (Ren et al., 2022).
Previous studies have shown a clear relationship between working shifts and adverse nursing outcomes including adverse events, physical discomfort, psychological problems and satisfaction (Bae & Fabry, 2014). Work scheduling is particularly important for nurses working in COVID‐19‐designated hospitals. Hence, to ensure scientific and reasonable work shifts, it is necessary to understand the shift status of frontline nursing staff in COVID‐19 units, and the relationship between working shifts and adverse nursing outcomes.
Previous studies have focused on nurses' working hours per shift. The NHC recommends that patients should be treated by classification, and isolation management and treatment sites will be determined according to their conditions. However, the policy was only written that nurses work a 4‐h shift to treat patients with COVID‐19 in intensive care units (ICUs) (NHC, 2020a). Several studies have reported the working hours of nurses in COVID‐19 wards (Zhang, Huang, & Guan, 2021). Seventeen journal articles were included in the target field, from the nurses in aiding Hubei Province, and four kinds of shift length, 2‐, 3‐, 4‐ and 6‐h shifts, were considered. The actual working hours and preferred working hours of nurses have also been reported (Zhang, Jiang, et al., 2021). However, there is a lack of a national, large‐sample survey on scheduling and adverse nursing outcomes in different units of COVID‐19‐designated hospitals.
Therefore, this study aimed to investigate scheduling in COVID‐19‐designated hospitals, including working hours, rest days, adverse nursing outcomes and their relationship. Additionally, the frontline nurses' advice on scheduling was also consulted. This study might provide a reference for the nursing dispatch of emergent major infectious diseases.
2. METHODS
2.1. Research design
Given the high risk of COVID 19, we conducted a cross‐sectional online survey between 11 April and 31 July 2020.
2.2. Setting and sample
In this study, 69 COVID‐19‐designated hospitals from 31 provincial administrative districts in mainland China were selected using stratified sampling. We ensured that at least one hospital in each province was included in this study. We restricted our staffing measure to frontline units, including fever clinics, observation wards, isolation wards (mild and severe) and ICUs. At least one head nurse and one line nurse from each department were required. The inclusion criteria for the participants were as follows: (a) Participants were required to manage the COVID‐19 unit or care for patients with COVID‐19 for more than a month, and (b) participants voluntarily participated in the study with informed consent.
2.3. Variables and measurements
We used a self‐designed questionnaire completed by nursing leaders and frontline nurses who reported data to measure the characteristics of the work setting, samples and work schedule. Data were derived from the following variables: (a) nursing allocation: average ratio of room or bed to nurse, professional titles, degrees, work seniority, work position nursing mode and human resource utilization; (b) work setting: categories of medical institutions, level of designated hospital and type of unit; (c) demographic features of the frontline nurse: gender, age, nationality, education, professional titles, working years, nursing subspecialty and number of rotation departments; (d) work schedule characteristics: work hours per shift and week, rest days and overtime; and (e) scheduling effect or nursing outcome: satisfaction on work scheduling (‘very dissatisfied’, or ‘dissatisfied’, or ‘average’, or ‘satisfied’, or ‘very satisfied’), excessive physical fatigue (‘yes’ or ‘no’), psychological distress (‘yes’ or ‘no’), competence leverage (‘never’, or ‘often’, or ‘always’), teamwork (‘poor’, or ‘average’, or ‘always’), nursing quality (‘totally incapable’, or ‘basically capable’, or ‘totally capable’), nursing adverse events (Mohsenpour et al., 2017) (‘yes’ or ‘no’) and work–life balance (‘never’, or ‘often’, or ‘always’). While completing the survey, frontline nurses were asked to make recommendations on nurse scheduling using the free text. The questionnaire was developed based on previous research (Lu et al., 2019; Min et al., 2019). Five participants (two head nurses and three nurses) with varied frontline units were invited to comment on the questionnaire, which we modified based on their comments. We then recruited 10 participants for a preliminary experiment using a convenience sampling method to evaluate the feasibility and approximate completion time of the questionnaire and further improve the questionnaire.
2.4. Data collection
The questionnaire was imported into a website called ‘Questionnaire Star’ (https://www.wjx.cn/). We sent the questionnaire to each participant in the form of a text message or WeChat, and each mobile phone could complete the questionnaire only once to avoid repeated submissions. All participating nurses were asked to submit questionnaires within 2 weeks of receiving them. After receiving the questionnaire, the research team members double‐checked the quality of the questionnaire, and if there was any ambiguity, we contacted the participants by telephone to verify the accuracy of the data. Indicators for checking the questionnaire were (a) no missing items, (b) match the true situation and (c) same question with no opposite answer. As the data were collected through online data entry procedures, all questions had to be answered to complete the survey. No data were missing.
2.5. Statistical analysis
Statistical analyses were conducted using SPSS Version 26 (IBM Corp, 2019). In descriptive analyses of hospitals, units, participants and nursing outcomes, quantitative data are presented as means or medians with interquartile ranges (IQRs), as appropriate, whereas categorical data are presented as frequencies and corresponding ratios. Continuous variables were classified by IQR as boundary values. Differences in work settings, demographic features of frontline nurses and work schedules among different nursing outcomes were analysed using the Wilcoxon rank‐sum test or Kruskal–Wallis rank‐sum test. Factors with a p value less than .05 in the above preliminary analysis were included in the logistic regression analysis. For satisfaction with work scheduling, competence leverage, teamwork, nursing quality and work–life balance, we used ordinal logistic regression. For physical fatigue, psychological distress and adverse events, we used binary logistic regression. Statistical significance was set at p < .05.
2.6. Ethical considerations
This study was approved by the Biomedical Ethical Committee of West China Hospital, Sichuan University (No. 2020‐514). As this study was an online survey, all respondents gave permission through signed electronic informed consent. Participation in the study was voluntary.
2.7. Role of funding source
The funder was not involved in the study design, data collection, data analysis, data interpretation or article writing.
3. RESULTS
3.1. Descriptive analysis of hospitals and units
Basic data were collected from 217 departments of 69 COVID‐19‐designated hospitals in 31 provinces in China. The designated hospitals were all equipped with nursing technology, protective materials and medical equipment to deal with the epidemic. The geographical distribution was categorized into seven major areas in China, and the number of designated hospitals is shown in Figure 1. The number of beds in these hospitals ranged from 200 to 8500, with a median of 1605. The number of nurses ranged from 82 to 6465, with a median of 1015. This study included 44 fever clinics and 173 inpatient isolation wards. Nurses in the severe isolation ward worked mainly for 4 h per shift, whereas those in the fever clinic and observation ward worked mainly for 6–8 h. Approximately half of the nurses had only 1 day of rest per week.
FIGURE 1.
Distribution and no. of the study participants
Forty‐four fever clinics were included in the analysis, with a total of 4972 patients per day. The total number of open rooms was 124 (1–14 per hospital). The median and mode were both 2, and the rooms of the two fever clinics were opened in 43.2% of the hospitals. The total number of nurses in the fever clinics was 797. The number of nurses in fever clinics in each hospital ranged from 4 to 220, with a median of 11, with 11.4% of hospitals having 11 nurses in fever clinics. The nurses in the fever clinics mainly include those with junior professional titles, bachelor's degrees, job responsibilities and over 10 years of work seniority. Seventeen fever clinics (38.6%) did not establish a post for hospital‐acquired infection control nurses. Most outpatient clinics (63.6%) had a reasonable utilization of human resources.
A total of 173 inpatient isolation wards were included in the study: 36 observation wards, 18 mild isolation wards, 37 ICUs and 82 mild and severe isolation wards. The number of nurses in inpatient isolation was 4808, and the number of beds was 6298. Primary holistic nursing is a mainstream mode of work. Nursing allocation in inpatient wards was similar to that in the fever clinics. A total of 24.3% (24/173) of inpatient isolation wards did not have hospital‐acquired infection control nurses. More than 20% of the wards showed excessive utilization of human resources.
3.2. Descriptive analysis of the characteristics of the work setting, samples and work schedule
As demonstrated in Table 1, 1356 frontline nurses were included in this study, mainly from provincial‐level (60.7%) and general (75.2%) COVID‐19‐designated hospitals, and distributed in different units. The age of the participants was skewed towards the younger group, with 50.2% in the ≤30‐year groups. As expected, the majority of participants were women (93.1%) and of Han nationality (89.6%). The educational background of these nurses was mainly bachelor's degree; however, in terms of quantity, nurses with junior professional titles were dominant. A total of 898 frontline nurses (66.2%) were mainly professionals with relevant COVID‐19 prevention and control backgrounds, whereas 26% of nurses had not been previously exposed to relevant professional knowledge.
TABLE 1.
The characteristics of work setting, schedule and the samples (N = 1356)
| Characteristics | n (%) |
|---|---|
| Work setting characteristics | |
| Categories of medical institutions | |
| General hospital | 1020 (75.2) |
| Infectious disease hospital | 336 (24.8) |
| Level of designated hospital | |
| Provincial‐level | 823 (60.7) |
| Municipal‐level | 496 (36.6) |
| County‐level | 37 (2.7) |
| Type of unit | |
| Fever clinics | 410 (30.2) |
| Observation ward | 161 (11.9) |
| Mild ward | 81 (6.0) |
| ICU | 245 (18.1) |
| Isolation ward (mild and severe) | 299 (22.1) |
| Observation and isolation ward | 160 (11.8) |
| Demographic features of the frontline nurse | |
| Gender | |
| Female | 1263 (93.1) |
| Male | 93 (6.9) |
| Age (years) | |
| 26 and below | 291 (21.5) |
| 27–30 | 389 (28.7) |
| 31–34 | 318 (23.5) |
| 35 and above | 358 (26.4) |
| Nationality | |
| Han | 1215 (89.6) |
| Minority nationality | 141 (10.4) |
| Educational background | |
| College degree | 293 (21.6) |
| Bachelor's degree | 1035 (76.3) |
| Master's degree or above | 28 (2.1) |
| Professional titles | |
| Junior | 975 (71.9) |
| Intermediate | 360 (26.5) |
| Senior | 21 (1.5) |
| Working years | |
| 4 and below | 325 (24.0) |
| 5–8 | 374 (27.6) |
| 9–12 | 314 (23.2) |
| 13 and above | 343 (25.3) |
| Nursing subspecialty | |
| Infectious nursing | 510 (37.6) |
| Respiratory nursing | 189 (13.9) |
| Critical care nursing | 106 (7.8) |
| Emergency nursing | 93 (6.9) |
| Other | 458 (33.8) |
| No. of rotations of relevant departments | |
| 0 | 352 (26.0) |
| 1 | 750 (55.3) |
| 2 | 204 (15.0) |
| 3 | 48 (3.5) |
| 4 | 2 (0.1) |
| Work schedule characteristics | |
| Work hours per shift (h) | |
| 4 and below | 294 (21.7) |
| 5–7 | 397 (29.3) |
| 8 and above | 665 (49.0) |
| Work hours per week (h) | |
| 28 and below | 346 (25.5) |
| 29–39 | 322 (23.7) |
| 40–42 | 388 (28.6) |
| 43 and above | 300 (22.1) |
| Rest days per week (days) | |
| 0 | 351 (25.9) |
| 1 | 177 (13.1) |
| 2 | 512 (37.8) |
| 3 and above | 316 (23.3) |
| Being unable to leave work because of overtime | |
| None | 214 (15.8) |
| A little (<20%) | 585 (43.1) |
| Less (20–40%) | 320 (23.6) |
| More (40–60%) | 155 (11.4) |
| Most (>60%) | 82 (6.0) |
| Scheduling effect characteristics | |
| Satisfaction on work scheduling | |
| Very dissatisfied | 3 (0.2) |
| Dissatisfied | 12 (0.9) |
| Average | 169 (12.5) |
| Satisfied | 603 (44.5) |
| Very satisfied | 569 (42.0) |
| Excessive physical fatigue | |
| No | 1206 (88.9) |
| Yes | 150 (11.1) |
| Psychological distress | |
| No | 1312 (96.8) |
| Yes | 44 (3.2) |
| Competence leverage | |
| Never | 15 (1.1) |
| Often | 738 (54.4) |
| Always | 603 (44.5) |
| Teamwork | |
| Poor | 20 (1.4) |
| Average | 297 (21.9) |
| Good | 1039 (76.6) |
| Scheduling to ensure nursing quality | |
| Totally incapable | 23 (1.7) |
| Basically capable | 634 (46.6) |
| Totally capable | 701 (51.7) |
| Adverse events | |
| No | 1304 (96.2) |
| Yes | 52 (3.8) |
| Work–life balance | |
| Never | 113 (8.3) |
| Often | 712 (52.5) |
| Always | 531 (39.2) |
Abbreviation: ICU, intensive care unit.
Almost half of the nurses reported working ≥8 h for shifts. A total of 84.2% of the nurses reported mandatory overtime, yet 25.9% had required on duty every day, indicating that the majority had some form of required extra working time, exceeding those that they were initially scheduled to work. The burden of work causes nurses to damage their physical and mental health, threatens the quality and safety of nursing and reduces nurses' satisfaction. The results of this study showed that 13.6% of nurses were dissatisfied with the scheduling. The work schedules and their effects are summarized in Table 1.
3.3. Univariate analysis of nurse‐related outcomes
Fifteen variables were successively included in the univariate analysis of satisfaction with work scheduling, physical fatigue, psychological distress, competence leverage, teamwork, nursing quality, adverse events and work–life balance. We found that the distribution among groups with different levels by medical institution, designated hospital, unit, gender, professional title, subspecialty, department rotation, working hours per shift and week, rest days and working overtime was different, as shown in Table S1.
3.4. Logistic regression analysis of nurse‐related outcomes
Factors with a p value less than .05 in the above preliminary analysis were included for further analysis. Table 2 summarizes the categories of medical institutions (odds ratio [OR], 0.682; 95% confidence interval [CI], 0.517–0.899), type of unit (fever clinics: OR, 2.067; 95% CI, 1.425–3.001; observation ward: OR, 1.765; 95% CI, 1.146–2.718), gender (female: OR, 0.494; 95% CI, 0.312–0.782), work hours (≤28: OR, 2.815; 95% CI, 1.701–4.660), rest days (2 days: OR, 0.417; 95% CI, 0.272–0.640) and working overtime (most [>60%] OR, 0.183; 95% CI, 0.109–0.306) that were associated with the level of nurse satisfaction.
TABLE 2.
Ordinal logistic regression analysis of factors associated with satisfaction level
| Explaining variables | B | SE | Wald | OR | 95% CI | p value |
|---|---|---|---|---|---|---|
| Categories of medical institutions (ref: infectious disease hospital) | −0.383 | 0.141 | 7.352 | 0.682 | (0.517–0.899) | .007** |
| Level of designated hospital (ref: county‐level) | ||||||
| Provincial‐level | −0.657 | 0.347 | 3.575 | 0.518 | (0.262–1.024) | .059 |
| Municipal‐level | −0.298 | 0.351 | 0.723 | 0.742 | (0.373–1.476) | .395 |
| Type of unit (ref: observation and isolation ward) | ||||||
| Fever clinics | 0.726 | 0.190 | 14.583 | 2.067 | (1.425–3.001) | .000** |
| Observation ward | 0.568 | 0.22 | 6.639 | 1.765 | (1.146–2.718) | .010* |
| Mild ward | −0.019 | 0.270 | 0.005 | 0.981 | (0.578–1.667) | .945 |
| ICU | 0.078 | 0.218 | 0.128 | 1.081 | (0.705–1.657) | .721 |
| Isolation ward (mild and severe) | 0.240 | 0.199 | 1.455 | 1.271 | (0.861–1.879) | .228 |
| Gender (ref: male) | −0.706 | 0.235 | 9.036 | 0.494 | (0.312–0.782) | .003** |
| Nursing subspecialty (ref: other) | ||||||
| Infectious nursing | −0.009 | 0.132 | 0.004 | 0.991 | (0.765–1.284) | .948 |
| Respiratory nursing | 0.147 | 0.182 | 0.653 | 1.158 | (0.811–1.654) | .419 |
| Critical care nursing | 0.016 | 0.241 | 0.005 | 1.016 | (0.634–1.631) | .946 |
| Emergency nursing | −0.101 | 0.231 | 0.192 | 0.904 | (0.575–1.422) | .661 |
| Work hours per shift (h) (ref: 8 and above) | ||||||
| 4 and below | −0.312 | 0.254 | 1.508 | 0.732 | (0.445–1.204) | .219 |
| 5–7 | 0.206 | 0.179 | 1.326 | 1.229 | (0.865–1.747) | .250 |
| Work hours per week (h) (ref: 43 and above) | 1.000 | |||||
| 28 and below | 1.035 | 0.257 | 16.188 | 2.815 | (1.701–4.660) | .000** |
| 29–39 | 0.2 | 0.212 | 0.891 | 1.221 | (0.807–1.850) | .345 |
| 40–42 | 0.108 | 0.172 | 0.390 | 1.114 | (0.795–1.560) | .532 |
| Rest days per week (days) (ref: 3 and above) | ||||||
| 0 | −0.373 | 0.196 | 3.619 | 0.689 | (0.470–1.011) | .057 |
| 1 | −0.332 | 0.18 | 3.389 | 0.717 | (0.504–1.021) | .066 |
| 2 | −0.874 | 0.218 | 16.089 | 0.417 | (0.272–0.640) | .000** |
| Being unable to leave work because of overtime (ref: none) | ||||||
| A little (<20%) | −0.952 | 0.170 | 31.567 | 0.386 | (0.277–0.538) | .000** |
| Less (20–40%) | −1.441 | 0.186 | 60.120 | 0.237 | (0.164–0.341) | .000** |
| More (40–60%) | −1.526 | 0.219 | 48.677 | 0.217 | (0.142–0.334) | .000** |
| Most (>60%) | −1.701 | 0.263 | 41.803 | 0.183 | (0.109–0.306) | .000** |
Note: Satisfaction level: 1 = very dissatisfied; 2 = dissatisfied; 3 = average; 4 = satisfied; and 5 = very satisfied. Likelihood ratio (LR) χ 2(25) = 191.756, p = .000, log likelihood = 3897.261, pseudo‐R 2 = .132. Parallelism test χ 2(75) = 76.233, p = .439.
Abbreviations: B, coefficient value; CI, confidence interval; ICU, intensive care unit; OR, odds ratio; ref, reference; SE, standard error.
p < .05.
p < .01.
Shorter rest days (OR, 1.289; 95% CI, 1.079–1.541) and more overtime work (OR, 1.419; 95% CI, 1.220–1.650) increased physical fatigue, whereas shorter rest days were associated with more psychological distress (OR, 1.447; 95% CI, 1.046–2.000). Female (OR, 1.709; 95% CI, 1.110–2.639), shorter rest days (OR, 1.656; 95% CI, 1.214–2.268) and more overtime work (OR, 2.597; 95% CI, 1.536–4.386) could affect nurses' competence leverage. The type of unit (isolation ward [mild and severe] OR, 1.933; 95% CI, 1.207–3.096), overtime work (OR, 0.291; 95% CI, 0.149–0.569), work (OR, 1.986; 95% CI, 1.052–3.747) and rest (OR, 0.394; 95% CI, 0.230–0.676) times per week were related to teamwork. Factors within the categories of medical institutions (OR, 0.743; 95% CI, 0.564–0.977), gender (female: OR, 0.511; 95% CI, 0.319–0.816), work hours per week (≤28: OR, 2.008; 95% CI, 1.188–3.394), rest days (≥3 days: OR, 0.416; 95% CI, 0.266–0.650) and working overtime (most [>60%] OR, 0.172; 95% CI, 0.099–0.298) were associated with nursing quality. Overtime was an independent significant predictor of adverse nursing events, with an OR of 1.487 (95% CI, 1.174–1.884). Being a man (OR, 1.727; 95% CI, 1.107–2.597), rotating more related departments (OR, 2.898; 95% CI, 1.530–5.485), taking longer breaks (OR, 1.418; 95% CI, 1.032–1.949) and working without overtime (OR, 6.289; 95% CI, 3.731–10.638) were more likely to lead to better work–life balance.
3.5. The frontline nurse's advice on scheduling
In total, 270 frontline nurses made 303 scheduling recommendations. Frontline nurses prefer that managers should focus on the following aspects of scheduling: working time, scheduling patterns, night shifts, rest and nurse preferences, as shown in Table 3.
TABLE 3.
The frontline nurse's advice on scheduling
| Category | Recommendation |
|---|---|
| Working time | Shorten working hours per shift to 4–6 h |
| Shorten working hours per shift below 4 h in ICU | |
| Shorten working hours per week below 40 h | |
| Avoid working overtime | |
| Scheduling patterns | Flexible scheduling |
| Team shift | |
| Shift rotation | |
| Add backup, mobile, coordinating or supporting positions | |
| Do not work days in a row and then take days off in a row | |
| Night shifts | Reduce night shift length to below 8 h |
| Reduce the frequency of night shifts | |
| Pay attention to hierarchy collocation and age collocation | |
| No consecutive night shift | |
| Rest | Guarantee time to rest, especially the night's rest |
| Take at least one day off per week, preferably two | |
| Nurse preferences | Ensure fairness |
| Ensure reasonable working hours | |
| Pay attention to physical and mental health, especially considering the body's tolerance |
Abbreviation: ICU, intensive care unit.
4. DISCUSSION
The COVID‐19 pandemic is a major public health emergency (Jee, 2020). The novel coronavirus is one of the most difficult viruses in human history, spreading faster and mutating more widely (Sharma et al., 2021). This is both a crisis and a major test in China and worldwide. Nursing managers face significant challenges, such as job responsibilities, workflow, work efficiency, workforce allocation and work shifts, all of which need to be improved in a short period of time to adapt to the current challenging situation (Bambi et al., 2020). This article mainly focused on the issue of nurse shifts, which is one of the most relevant and pressing issues for frontline nurses.
4.1. Nursing allocation
This study surveyed the allocation of care in different epidemic prevention departments in 31 provinces and municipalities in China. Among them, the ICU had the highest allocation of human resources. This may be related to the fact that the nurse–patient ratio in the ICU is 1:0.5, which is specifically published by the government (NHC, 2020b). The findings showed that nurses in all types of departments were relatively young, in terms of both their professional title and working years. The results are similar to those reported by other researchers (Beckett et al., 2021; Ren et al., 2022). We found that the post setting of hospital‐acquired infection control nurses increased in the department; however, not all departments had set this post. To the best of our knowledge, no studies have been conducted on the setting of nurses' posts in COVID‐19 wards. However, this position is very important based on past experience, which is mainly responsible for hospital infection management and monitoring in the undergraduate department, and not only in the infectious ward. A systematic review confirmed the value of hospital‐acquired infection control nurses in preventing infection and reducing the financial burden (Cohen et al., 2016). Therefore, we strongly recommend that hospital infection prevention and control posts should be established in all anti‐epidemic departments.
4.2. Nursing scheduling and adverse nursing outcomes
This study discussed the status of nurses' shifts, including working hours, days off and overtime. Nurses in the severe isolation ward worked mainly for 4 h per shift, whereas those in the fever clinic and observation ward worked mainly for 6–8 h. Compared with the era before the epidemic, the new crown epidemic caused nurses to work less effectively, especially in the ICU. Working on the frontline of medical care, nurses have a lot of tedious daily work. They have the most opportunities, longest time and most stress with patients. They are also the frontline for saving lives. At the same time, they also face the need for protective measures, heavy workload, multiple psychological and stress reactions, and other pressures and challenges. The length of working hours has a great impact on nursing quality (Zhang, Jiang, et al., 2021) and nurses' feelings (Lu et al., 2019; Min et al., 2022). We found that, as the total number of working hours increased, nurses' satisfaction levels, teamwork and quality of care declined. This may be related to long‐term wearing of PPE (Ortega et al., 2020). Most existing studies focus on the working hours (James et al., 2021; Zhan et al., 2020), but there are a few studies on rest. Min et al. (2020) reported that break length had an indirect effect on patient safety, medication errors and falls with injury through missed nursing care. Approximately half of the nurses had only one or no day for rest per week, which seriously affected their work and life. Our results provided evidence that a lack of adequate rest led to a decline in nurses' work competence, satisfaction with work scheduling and teamwork, and an inability to balance work and life, which affected their physical and mental health. Long working hours and lack of rest time led to serious overtime work for frontline nurses. COVID‐19 has led to nurses working in stressful environments and being asked to work mandatory overtime (Squellati & Zangaro, 2022). We reported negative effects of working overtime on adverse nursing outcomes. Studies from Sweden and Iran have also confirmed this result (Jamebozorgi et al., 2022; Nymark et al., 2022). Arranging the work and rest time of nurses scientifically and reasonably in such an urgent situation is a great challenge faced by nursing managers. We also found that the work setting characteristics and demographic features of frontline nurses were associated with adverse nursing outcomes. Governments and hospitals should consider these factors when selecting nurses to fight COVID‐19.
4.3. Advances in scheduling
We also conducted a summary content analysis of frontline nurses' recommendations for shifts. In addition to expressing recommendations on shortening working hours and ensuring adequate rest, frontline nurses offered perspectives on shift patterns, willingness and night shifts. Uncertainties and exigencies that differ from those of routine clinical care arise during the COVID‐19 outbreak (Gao et al., 2020). We found that nurses would like to be allowed flexibility while maintaining a basic shift structure. Studies have shown that flexible scheduling can optimize shifts, improve work efficiency (Dall'Ora et al., 2020), ensure that nurses have enough time to rest and better ensure patient safety and quality of care (Butler et al., 2019). In terms of night shifts, nurses responded by reducing the frequency of shifts and staffing. With the shortage of nursing manpower and the need for young nurses to work night shifts, nurses have less experience in nursing care than proficient nurses, so they hoped to work with colleagues of different levels and ages to ensure quality of care and reduce the frequency of work. As mentioned in this study, nurses in China tended to be younger, which is the main force in the fight against the epidemic (Ren et al., 2022). The COVID‐19 pandemic has made us aware that the nursing workforce is getting increasingly younger. We cannot choose the age of nurses, but we can make two suggestions: First, we can train young nurses, and dealing with major new infectious diseases must become their basic competence, which has a long way to go; second, we can create a pool of nurses and move skilled nurses into positions where they are needed. Moreover, fairness and rationality have always been important issues in shift work (Winslow et al., 2019), and this outbreak is not an exception (Meese et al., 2021). Nursing managers must create scheduling patterns and implement off‐duty institutionalization. In brief, nursing managers should engage in more scientific and humanized scheduling to ensure fairness and rationality.
4.4. Strengths and limitations
The strengths of this study are that it was the first nationwide study with a large sample, covering 31/34 provinces, municipalities or autonomous regions in China. Additionally, the subjects included in this study were frontline nurses and head nurses from all departments involved in the treatment of patients with COVID‐19 (fever clinic, observation ward, isolation ward and intensive care ward). Although a previous paper reported issues, it only included information about nursing work hours per shift from the Wuhan and Guizhou provinces (Zhang, Jiang, et al., 2021). In addition, this study added content analysis based on a quantitative study to supplement frontline nurses' suggestions on shifts. The results provide evidence of how nursing managers arrange nurse schedules scientifically and reasonably to reduce the occurrence of adverse nursing outcomes.
However, this study has some limitations. First, convenience sampling is performed. The number and level of hospitals selected by each province were inconsistent, and the number of nurses surveyed by each hospital was also different, which may have led to a selection bias and restricted extrapolation. Second, each hospital had different types of isolation wards, including mild, severe, and mild and severe isolation wards. As different types of wards have an impact on scheduling (Bergman et al., 2021), they were not combined. Finally, this study used an online questionnaire. Although it is known that there are differences between day and night shifts (James et al., 2021), considering that most hospitals adopt fixed shifts, this study only investigated the working hours of frontline nurses on day shifts, which may lead to measurement bias.
5. CONCLUSIONS
Nurses are the main forces in the fight against COVID‐19. Scheduling is one of the most relevant and pressing issues faced by frontline nurses. This study revealed that long working hours, lack of adequate rest time and overtime can seriously affect the quality and safety of nurses' work, resulting in adverse outcomes. Frontline nurses hope that scheduling guarantees the time to rest while ensuring fairness. The government, hospitals and administrators still face many problems that need to be overcome in nursing scheduling. Overall, this first national study on scheduling for frontline nurses provides insight into possibilities for nursing science and rational nursing dispatching to effectively deal with public health emergencies.
6. IMPLICATIONS FOR NURSING MANAGEMENT
Governments and hospitals should also consider these factors, such as the work setting characteristics and demographic features of frontline nurses, when selecting nurses to fight COVID‐19. Nurses work 4–6 h per shift. It is recommended that during the epidemic, nurses routinely work 4 h per shift. When the environment is hot or protective equipment is tight, nurses are recommended to work 6 h per shift, but this cannot be used as a long‐term working state. Nurses have different working hours in different types of coronavirus units; therefore, nursing managers should consider the working environment and needs on the nursing schedule. In the future, we should pay attention to nurses' fairness while ensuring their rest.
CONFLICTS OF INTEREST
The authors declare that there are no conflicts of interest.
ETHICAL CONSIDERATIONS
The study was approved by the Biomedical Ethical Committee of West China Hospital, Sichuan University (No. 2020‐514). Because this study was an online survey, all respondents gave permission by signed electronic informed consent. Participation was voluntary.
Supporting information
Data S1. Supporting Information
ACKNOWLEDGEMENTS
We would like to thank all the hospitals and participants for their efforts and time and Prof. Yu Jiajie and Prof. Fang Fang from West China Hospital of Sichuan University for their guidance on this paper. We pay tribute to our nursing colleagues who have been and are fighting on the frontlines of the COVID‐19 response.
Liu, S. , Wang, C. , Jiang, Y. , Ren, H. , Yu, T. , Cun, W. , & Yang, Z. (2022). Nurse scheduling in COVID‐19‐designated hospitals in China: A nationwide cross‐sectional survey. Journal of Nursing Management, 30(8), 4024–4033. 10.1111/jonm.13832
Funding information This study was supported by the National Natural Science Foundation of China (No. 72174135), which was not involved in the study design, data collection, data analysis, data interpretation or article writing.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1. Supporting Information
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.