BACKGROUND: Nursing home staff often face challenges in achieving a satisfactory work-life balance, particularly because of the nature of shift work. While long shifts offer extended periods off work, their impact on the delicate balance between work and leisure remains understudied in the context of nursing homes. This study investigated the experiences of nursing home staff in Norway working long shifts lasting 12-14 h and their perceptions of the balance between family life and work.
METHODS: Eighteen nursing home staff members were interviewed following a semi-structured qualitative approach. The participants worked in three types of long shifts and provided insights into their experiences, addressing issues such as work hours, shift patterns, and work-family balance.
RESULTS: The study revealed four main categories: (1) impact of long shifts on family life-the highs and lows; (2) maximizing time off with long shifts; (3) reducing job stress with long shifts; and (4) full-time work leads to predictable hours and stable income. The participants emphasised the distinct separation between work and leisure during long shifts, acknowledging limited social life during working periods but appreciating extended periods off. Family life posed challenges, especially with young children, but the participants found benefits in the longer periods of family time during days off. Longer rest periods and reduced commuting time were perceived as advantages of long shifts, contributing to better sleep, reduced stress and overall well-being. Long shifts also allowed for more predictable working hours and income, supporting a stable work-life balance.
CONCLUSIONS: Balancing work and family life involves more than just the number of hours spent at work; it also encompasses the quality of those hours both at work and at home. Our findings underscore the complex interplay between work and family life for nursing home staff working long shifts. While challenges exist, benefits such as extended time off, improved sleep, reduced stress, and predictable working hours contribute positively to their work-life balance. Long shifts in nursing homes offer a unique perspective on achieving work-life balance, revealing both the challenges and advantages inherent in such schedules. Understanding the experiences of nursing home staff in this context can inform future innovations in shift scheduling, promoting a more balanced and sustainable work environment for healthcare professionals. For some healthcare staff, extended shifts can lead to a better work-life balance.

OBJECTIVE: To characterize the extent of the impact of the COVID-19 pandemic on aspects of the anesthesia residency experience from the perspective of American College of Veterinary Anesthesia and Analgesia (ACVAA) mentors and trainees. This includes the residency interview process, didactic training and availability and variety of clinical cases for resident learning.
METHODS: Cross-sectional, online, anonymous voluntary survey.
METHODS: A total of 58 respondents, representing 30 residency program mentors and 28 residents.
METHODS: Surveys were distributed by email to 94 residency program mentors and 70 residents, representing programs and residents registered with the ACVAA during the years of 2019-2021. Anonymous links to an online survey were provided via a link embedded in the email. Questions included perceived impacts (-5 to +5 scale) on didactic and clinical training, case distribution, workload, residency application and selection process, and mental health. Responses were summarized with descriptive statistics [median (IQR) or mean ± SD].
RESULTS: The overall survey response rate was 31.9% (30/94) and 40.0% (28/70) for resident program mentors and residents, respectively. There was a negative perceived impact on overall residency training reported: -1.0 (IQR -2.0 to 0) for program mentors and -1.5 ± 1.5 for residents. The reported perceived impact of travel limitations on case log completion was -2.0 (IQR -4.0 to -1.0) for program mentors and -2.4 ± 2.0 for residents. Program mentors and residents had incongruent feelings on the impact of the pandemic on work hours, with residents reporting a perceived increase in work hours [+2.1 ± 2.1 versus 0 (IQR -1.0 to 0)].
CONCLUSIONS: Residency program mentors and residents generally agreed that the COVID-19 pandemic had negatively impacted multiple aspects of residency interview and training process. A broader, objective analysis of veterinary anesthesia training programs is required to fully elucidate the scale of the impact on veterinary anesthesia training.

BACKGROUND: Otorhinolaryngology (ORL) specialists treat patients of different ages, ranging from elderly patients with head and neck tumors to neonates with respiratory problems. No studies have been conducted to explore the factors that affect the preference for ORL among Umm Al-Qura University students. We aimed to investigate factors and motivators influencing medical students at Umm Al-Qura University in Makkah, Saudi Arabia, who choose to pursue a career in ORL-head and neck surgery.
METHODS: This cross-sectional study was conducted over two months in 2023 among 439 students in the pre-clinical, clinical, and internship years of the Faculty of Medicine at Umm Al-Qura University, Saudi Arabia. We shared a Google Forms questionnaire (Google, Inc., Mountain View, CA) and explored factors associated with interest in ear, nose, and throat (ENT) as a future specialty. We used Pearson\'s chi-square test to arrive at the results.
RESULTS: A total of 339 (77.2%) participants were female. Participants were evenly divided between pre-clinical years (213 (48.5%)) and clinical years (207 (47.2%)), with a smaller percentage in the internship category (19 (4.3%)). ORL involvement was reported in 159 (36.2%) of the participants.
CONCLUSIONS: The surgical specialty of ORL focuses mostly on conditions affecting the head, neck, nose, and ears. Since students found this specialty fascinating, we recommend that senior doctors make a greater effort to enlighten doctors-in-training about this field of expertise through lectures and campaigns at hospitals and universities.

We examine the effects of the Affordable Care Act Medicaid expansions on the employment and work hours of nursing assistants (NAs). We use the 2011-2019 American Community Survey data to identify NAs likely to be affected by Medicaid expansions (income up to 138% of the federal poverty level) in nursing homes and hospitals. Using classical difference-in-differences regressions, we find that Medicaid expansions have little effect on employment and work hours among NAs in the full sample. However, there is a 4.4 percentage-point increase in the probability of working part-time (<30 hours/week) for nursing home NAs (p < .05). We found no employment effects of Medicaid expansions for hospital NAs. Our study adds to the literature on the heterogeneous effects of Medicaid expansions on work effort across occupations and workplaces. The rise in part-time employment for nursing home NAs following Medicaid expansions suggests the need for improved benefits to encourage full-time employment.

Objectives: This study examined the impact of nurse staffing, working hours, mandatory overtime, and turnover on nurse outcomes in acute care hospitals. Previous studies have focused on the single characteristics of sub-optimal nurse staffing but have not considered them comprehensively. Methods: Data were collected in July-September 2022 using convenience sampling and an online survey (N = 397). For the analysis, 264 nurses working as staff nurses at 28 hospitals met the inclusion criteria. Univariate analysis and multivariable generalized estimating equation (GEE) were performed. Results: Both nurse staffing (β = -0.036, standard error [SE] = 0.011) and turnover (β = -0.006, SE = 0.003) were significant factors affecting job satisfaction. In the multivariable GEE, only mandatory overtime (β = 0.395, SE = 0.116) was significantly related to intent to leave. Nurse staffing, work hours, mandatory overtime, and turnover were not significantly related to burnout. Subjective health status and workload were significantly associated with burnout. Conclusion: Nurse staffing policies and improvement programs in hospitals should be implemented to improve nurses\' job satisfaction. Labor policy should ban mandatory overtime.

In this study, we review the initial impacts of the coronavirus disease 2019 (COVID-19) pandemic on the Canadian labour market. We focus on changes in employment and aggregate hours worked between February 2020 and April 2020 while accounting for normal monthly changes in these indicators. We find that COVID-19 induced a 32 percent decline in aggregate weekly work hours among workers aged 20-64 years, alongside a 15 percent decline in employment. We characterize the distribution of work lost, finding that nearly half of job losses are attributed to workers in the bottom earnings quartile. Those most affected by COVID-19 are in public-facing jobs in industries most affected by shutdowns (accommodation and food services), younger workers, paid hourly, and non-union. The results provide context for policy development, with both supply and demand sides of the labour market to consider.
Les auteurs examinent les premiers impacts de la pandémie de la COVID-19 sur le marché du travail canadien. Ils se concentrent sur les changements en matière d’emploi et de nombre total d’heures travaillées entre février 2020 et avril 2020, en tenant compte des variations mensuelles normales de ces indicateurs. Ils constatent que la crise économique liée à la COVID‑19 a entraîné une diminution de 32 pour cent du nombre total d’heures de travail hebdomadaires et de 15 pour cent des emplois chez les personnes âgées de 20 à 64 ans. Ils analysent la répartition des emplois perdus, constatant que près de la moitié des pertes d’emploi sont attribuées aux travailleurs du quartile inférieur des gains. Les baisses d’emploi les plus marquées ont été enregistrées dans les industries les plus touchées par les fermetures, dans lesquelles la proximité physique est importante (services d’hébergement et de restauration), de même que parmi les jeunes travailleurs et les personnes occupant des emplois rémunérés à l’heure et non syndiqués. Les résultats de l’étude fournissent un contexte pour l’élaboration de politiques publiques assurant la prise en compte des changements tant du côté de l’offre que de celui de la demande de travail.

Objective  To examine the relationship between nurses\' sleep quality and their tendency to commit medical errors. Materials and Methods  The research was conducted in a state hospital, a private hospital, and a university hospital in a province located in the west region of Turkey (n = 378 nurses) between September 2020 and October 2021. Data was collected using a sociodemographic data form, the Medical Error Tendency Scale in Nursing (METSN), and the Pittsburgh Sleep Quality Index (PSQI). Verbal and written consent were obtained from the nurses who participated in the study. The data were collected through face-to-face interviews. Results  The mean score of the nurses was of 8.25 ± 4.81 on the PSQI and of 230.29 ± 14.15 on the METSN. A significant difference was found regarding age, marital status, level of schooling, weekly working hours, and the shift schedule of nurses and their sleep quality ( p  < 0.001). A significant difference was found regarding age, marital status, level of schooling, the hospital the nurses worked in, and the tendency to commit medical errors ( p  < 0.001). There was a statistically significant negative and moderate correlation between the PSQI and METSN scores ( p  < 0.001; r = -0.548). Conclusion  The tendency of nurses to commit medical errors was determined as low, and their sleep quality was poor. We have also determined that, as the sleep quality worsened, the tendency of nurses to commit medical errors increased.

BACKGROUND: Atypical temporal work patterns such as working longer than the standard 35-40 h/ week, weekend working, and nonstandard work schedules (i.e. outside of the typical 9-5, including but not restricted to shiftwork) are increasingly prevalent in the UK. Aside from occupation-specific studies, little is known about the effects of these atypical temporal work patterns on sleep among workers in the UK, even though poor sleep has been linked to adverse health problems, lower workplace productivity, and economic costs.
METHODS: We used regression models to investigate associations between three types of atypical temporal work patterns (long and short weekly work hours, weekend working, and nonstandard schedules) and sleep duration and disturbance using data from over 25,000 employed men and women from 2012-2014 and/or 2015-2017 in the UK Household Longitudinal Study, adjusting for potential confounders and psychosocial work factors.
RESULTS: We found that relative to a standard 35-40 h/week, working 55 h/week or more was related to short sleep (less than 7 h/night) and sleep disturbance. Working most/all weekends compared to non-weekends was associated with short sleep, long sleep (more than 8 h/night), and sleep disturbance, as was working nonstandard schedules relative to standard schedules (fixed day-time schedules). Further analyses suggested some gender differences.
CONCLUSIONS: These results should prompt employers and policymakers to recognise the need for rest and recovery, consider how the timing and scheduling of work might be improved to better support workers\' health and productivity, and consider appropriate compensation for anyone required to work atypical temporal work patterns.

Traditional methods for investigating work hours rely on an employee\'s physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work.
In this study, we aimed to develop a novel approach called \"probability in work mode,\" which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours.
To capture human-smartphone interactions and GPS locations, we used the \"Staff Hours\" app, developed by our team, to passively and continuously record participants\' screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work.
Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes.
Our novel approach, the probability in work mode, harnessed human-smartphone interaction patterns and machine learning models to enhance the precision and accuracy of work hour investigation. By integrating human-smartphone interactions and GPS data, our method provides valuable insights into work patterns, including remote work and breaks, offering potential applications in optimizing work productivity and well-being.

Objectives: We expressed the combined effect by the ratio of daily sleep time to daily work time. The aim of this study was to discussed the predictive ability of daily sleep hours/work hours (SH/WH) ratio for diabetes risk. Methods: Cox proportional hazards regression was used to calculate the hazard ratios (HRs) of new-onset diabetes. Restricted cubic spline analyses were performed to visualize the influence trend of SH/WH ratio and diabetes risk. Results: The RCS model revealed a non-linear and L-shaped correlation between SH/WH ratio and diabetes risk. Compared with the participates with SH/WH ratio <1, those with a ratio ≥1 had a lower risk of developing diabetes. The multivariable adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) of new-onset diabetes in Q2, Q3, Q4 and Q5 groups compared with Q1 group were 0.82 (0.57, 1.19), 1.05 (0.69, 1.59), 0.57 (0.36, 0.91), 0.66 (0.42, 1.06). The Kaplan-Meier curve showed that Q4 group had lower cumulative incidence. Conclusion: Sleeping longer than working (SH/WH ratio ≥1) can reduce risk for developing diabetes. A minimal risk observed at 1.10-<1.37 (the fourth quintile) of SH/WH ratio.