Effects of modifications to the health and social sector's collective agreement on the objective characteristics of working hours

Sequence analysis of the combinations of work shifts and absences in health care - comparison of two years of administrative data

BACKGROUND

In health care, the shift work is arranged as irregular work shifts to provide operational hours for 24/7 care. We aimed to investigate working hour trends and turnover in health care via identification of time-related sequences of work shifts and absences among health care employees. The transitions between the work shifts (i.e., morning, day, evening, and night shifts), and absences (days off and other leaves) over time were analyzed and the predictors of change in irregular shift work were quantified.

METHODS

A longitudinal cohort study was conducted using employer-owned payroll-based register data of objective and day-to-day working hours and absences of one hospital district in Finland from 2014 to 2019 (n = 4931 employees). The working hour data included start and end of work shifts, any kind of absence from work (days off, sickness absence, parental leave), and employee's age, and sex. Daily work shifts and absences in 2014 and 2019 were used in sequence analysis. Generalized linear model was used to estimate how each identified sequence cluster was associated with sex and age.

RESULTS

We identified four sequence clusters: "Morning" (60% in 2014 and 56% in 2019), "Varying shift types" (22% both in 2014 and 2019), "Employee turnover" (13% in 2014 and 3% in 2019), and "Unstable employment (5% in 2014 and 19% in 2019). The analysis of transitions from one cluster to another between 2014 and 2019 indicated that most employees stayed in the same clusters, and most often in the "Varying shift types" (60%) and "Morning" (72%) clusters. The majority of those who moved, moved to the cluster "Morning" in 2019 from "Employee turnover" (43%), "Unstable employment" (46%) or "Varying shift types" (21%). Women were more often than men in the clusters "Employee turnover" and "Unstable employment", whereas older employees were more often in "Morning" and less often in the other cluster groups.

CONCLUSION

Four clusters with different combinations of work shifts and absences were identified. The transition rates between work shifts and absences with five years in between indicated that most employees stayed in the same clusters. The likelihood of a working hour pattern characterized by "Morning" seems to increase with age.

Working hour characteristics in the Finnish retail sector - a registry study on objective working hour data

Earlier research is lacking on the prevalence and nature of objective working hour characteristics in the retail sector. We developed a codification in the retail sector and investigated differences in objective working hour characteristics by part-time work, sex and age. The payroll-based registry data of objective working hours consisted >12,000 employees of the retail sector in Finland for 2018-2020. Descriptive statistics for means, standard deviations (SD) and range of annual working hour characteristics were calculated, the differences in means were tested, partially based on the protocol established for health care sector. The final sample had 60-63% part-time employees and 23% men. Morning shifts were more frequent (48-51%) among full-time employees compared to 27-30% of the part-time employees. Evening shifts, 43-46%, were frequent among part-time employees vs. 26% in full-time. No sex differences were detected, and age group differences only among part-time employees. To conclude, the codification for registry-based working hour data enables us to identify individual differences in working hour characteristics. The working hour characteristics differed between part-time and full-time employees, not between sexes, whereas age differences were minor and among part-time employees. The codification could be used in studies of the retail sector in association with health and wellbeing.

Patterns of working hour characteristics and risk of sickness absence among shift-working hospital employees: a data-mining cohort study

OBJECTIVES

Data mining can complement traditional hypothesis-based approaches in characterizing unhealthy work exposures. We used it to derive a hypothesis-free characterization of working hour patterns in shift work and their associations with sickness absence (SA).

METHODS

In this prospective cohort study, complete payroll-based work hours and SA dates were extracted from a shift-scheduling register from 2008 to 2019 on 6029 employees from a hospital district in Southwestern Finland. We applied permutation distribution clustering to time series of successive shift lengths, between-shift rest periods, and shift starting times to identify clusters of similar working hour patterns over time. We examined associations of clusters spanning on average 23 months with SA during the following 23 months.

RESULTS

We identified eight distinct working hour patterns in shift work: (i) regular morning (M)/evening (E) work, weekends off; (ii) irregular M work; (iii) irregular M/E/night (N) work; (iv) regular M work, weekends off; (v) irregular, interrupted M/E/N work; (vi) variable M work, weekends off; (vii) quickly rotating M/E work, non-standard weeks; and (viii) slowly rotating M/E work, non-standard weeks. The associations of these eight working-hour clusters with risk of future SA varied. The cluster of irregular, interrupted M/E/N work was the strongest predictor of increased SA (days per year) with an incidence rate ratio of 1.77 (95% confidence interval 1.74-1.80) compared to regular M/E work, weekends off.

CONCLUSIONS

This data-mining suggests that hypothesis-free approaches can contribute to scientific understanding of healthy working hour characteristics and complement traditional hypothesis-driven approaches.

Ageing shift workers' sleep and working-hour characteristics after implementing ergonomic shift-scheduling rules

We studied whether implementing binding ergonomic shift‐scheduling rules change ageing (≥45 years) social and healthcare employees’ (mean age 52.5 years, 95% women) working‐hour characteristics (e.g. weekly working hours, number and length of night shifts, and short shift intervals) and sleep. We compared an intervention group (n = 253) to a control group (n = 1,234) by survey responses (baseline 2007/2008, follow‐up 2012) and objective working‐hour characteristics (intervention group n = 159, control group n = 379) from 91 days preceding the surveys. Changes in working‐hour characteristics were analysed with repeated measures general linear models. The fully adjusted model (sociodemographics and full‐/part‐time work) showed that proportion of short shift intervals (<11 hr, p = .033) and weekend work (p = .01) decreased more in the intervention than in the control group. Changes in sleep outcomes were analysed with generalised logit model to binomial and multinomial variables. The fully adjusted model (sociodemographics, full‐/part‐time work, job strain, health behaviours, and perceived health) revealed higher odds in the intervention group for long sleep (≥9 hr; odds ratio [OR] 5.53, 95% confidence interval [CI] 2.21–13.80), and lower odds of short sleep (<6 hr; OR 0.72, 95% CI 0.57–0.92), having at least two sleep difficulties often (OR 0.55, 95% CI 0.43–0.70), and more specifically difficulties in falling asleep (OR 0.56, 95% CI 0.41–0.77), waking up several times per night (OR 0.43, 95% CI 0.34–0.55), difficulties in staying asleep (OR 0.64, 95% CI 0.49–0.82), and non‐restorative sleep (OR 0.70, 95% CI 0.54–0.90) than the control group. In conclusion, implementation of ergonomic shift‐scheduling rules resulted in minor changes in ageing employees’ objective working hours and a consistent buffering effect against worsening of sleep.

Working time dimensions and well-being: a cross-national study of Finnish and German health care employees

Health care professionals often face irregular working hours and high work pace. We studied associations of the five working time dimensions duration (weekly working hours), timing (shift work and weekend work), on-call work, working time autonomy, and work tempo (deadline and performance pressure) with well-being among health care employees in Finland and Germany. We used data on working time dimensions and indicators of well-being (work-life conflict, poor perceived health, sleep difficulties, and fatigue) from a cohort of 5050 hospital employees (Working Hours in the Finnish Public Sector Study 2015, WHFPS) and 1450 employees in the health care sector in Germany responding to the German BAuA-Working Time Survey in 2015 (BAuA-WTS). Findings from logistic regression analyses showed that high work tempo was associated with increased work-life conflict (WHFPS: odds ratio [OR] = 3.64, 95%CI 3.04-4.36 and BAuA-WTS: OR = 2.29, 95%CI 1.60-3.27), sleep difficulties (OR = 1.75, 95%CI 1.43-2.15 and OR = 1.33, 95%CI 1.03-1.71) and fatigue (OR = 2.13, 95%CI 1.77-2.57 and OR = 1.64, 95%CI 1.29-2.10) in both datasets. Weekend work was associated with increased work-life conflict (OR = 1.48, 95%CI 1.27-1.72 and OR = 1.61, 95%CI 1.12-2.32); and high working time autonomy with decreased work-life conflict (control over the timing of breaks: OR = 0.65, 95%CI 0.55-0.78 and OR = 0.52, 95%CI 0.33-0.81). The associations between other working time dimensions and well-being were less consistent. These results suggest that tight deadlines, performance pressure, weekend work and lack of working time autonomy are linked to impaired well-being among health care employees.

The effects of using participatory working time scheduling software on working hour characteristics and wellbeing: A quasi-experimental study of irregular shift work

BACKGROUND

Studies in the health care sector indicate that good work time control is associated with better perceived wellbeing but also with non-ergonomic work schedules, such as compressed work schedules. Participatory working time scheduling is a collaborative approach to scheduling shift work. Currently, there is a lack of information on whether working hour characteristics and employees' wellbeing in irregular shift work change after implementing participatory working time scheduling.

OBJECTIVE

To investigate the effects of using digital participatory working time scheduling software on working hour characteristics and well-being among Finnish hospital employees.

PARTICIPANTS AND METHODS

We compared changes in objective working hour characteristics and wellbeing between 2015 and 2017 among employees (n = 677, mainly nurses and practical nurses) when using participatory working time scheduling software (participatory scheduling, n = 283) and traditional shift scheduling (traditional scheduling, n = 394). The statistical analyses were conducted using the repeated measures general linear model and the generalized logit model for binomial and multinomial variables adjusted for age, sex, education, shift work experience, control over scheduling of shifts at baseline (where applicable) and hospital district.

RESULTS

The proportion of long work shifts (≥ 12 h) increased to a greater extent (F = 4.642, p = 0.032) with the participatory scheduling than with the traditional scheduling. In comparison to traditional scheduling, the perceived control over scheduling of shifts increased (OR 3.24, 95% CI 1.73-6.06) and excessive sleepiness in connection with evening shifts decreased (OR 0.40, 95% CI 0.16-0.99) significantly with participatory scheduling. None of the other wellbeing variables showed statistically significant changes in the adjusted models.

CONCLUSIONS

The proportion of long work shifts and perceived control over scheduling of shifts increased more among employees using participatory working time scheduling than among those using traditional scheduling. Otherwise, using participatory working time scheduling software had little effect on both objectively measured working hour characteristics and perceived wellbeing in comparison to traditional scheduling. The results merit confirmation in a larger sample with a longer follow-up. Tweetable abstract Participatory working time scheduling combines individual flexibility and staffing requirements in shift work.

Working hour characteristics and schedules among nurses in three Nordic countries - a comparative study using payroll data

Background

Organisation of working hour schedules in the Northern European countries are rather similar. EU countries are obliged to adopt national legislation regarding duration of weekly working hours and rest periods. Yet, working hour characteristics and schedules are likely to differ with respect to starting times and duration depending e.g. on culture and tradition. Yet, very little is known about potential differences between shifts and schedules across countries among nursing personel. This knowledge is relevant, since the potential differences in working hour characteristics may influence and possibly explain some of the differences observed in studies of health and safety.The aim of the study was to compare characteristics of working hours and work schedules among nursing personel in three Nordic countries: Denmark, Finland and Norway.

Methods

The study populations included nursing personnel holding a ≥ 50% position at public hospitals in Denmark (n = 63,678), Finland (n = 18,257) or Norway (n = 1538) in 2013. Objective payroll based registry data with information on daily starting and ending times were used to compare working hour characteristics e.g. starting time, duration of shift, and quick returns (< 11 h between two shifts), as well as work schedules e.g. permanent or 3-shift work between the three countries.

Results

Night shifts generally started earlier and lasted longer in Finland (10-11 h starting at 20:00-22:59) than in Norway (10 h starting at 21:00-21:59) and in Denmark (8 h starting at 23:00-23:59). Very long shifts (≥12 h) were more common in Denmark (12%) compared to Finland (8%) and Norway (3%). More employees had many (> 13/year) quick returns in Norway (64%) and Finland (47%) compared to Denmark (16%). The frequency of 3-shift rotation workers was highest in Norway (41%) and lower in Denmark (22%) and Finland (22%). There were few differences across the countries in terms of early morning shifts and (very) long weekly working hours.

Conclusion

Despite similar distribution of operational hours among nurses in the three countries, there were differences in working hour characteristics and the use of different types of work schedules. The observed differences may affect health and safety.

Exposure to working-hour characteristics and short sickness absence in hospital workers: A case-crossover study using objective data

INTRODUCTION

Shift work characteristics, such as the number of night shifts or quick returns, are linked to disturbed sleep and greater work-life conflict, but little is known about their association with short sickness absences. Studies utilizing objective working-hour characteristic data can provide knowledge on specific shift work characteristics that may associate with sick leave. We investigated the association between working-hour characteristics in shift work and the incidence of short (1-3 days) sickness absences.

PARTICIPANTS AND METHODS

The individual-level data were collected from the employers' electronic working time records of the Finnish Public Sector study. The final analytical sample was restricted to the first incidence of short sickness absence (1-3 days) from January 1, 2008 to the end of 2015. We studied employees with shift work contracts (n = 12 156) and day work contracts (n = 6225). Of these, 89% were female workers, with a mean age of 37.1 years, and 12% worked part-time. We calculated type of shift (morning, day, evening, and night), quick returns (< 11 h between two work shifts), length of work shift and weekly working time. We utilized case-crossover study design to compare the working-hour characteristics of the 28 days preceding sickness absence (exposure window) and those of the 28 days before the exposure window (control window), using conditional logistic regression models for odds ratios with 95% Confidence Intervals (95%CI).

RESULTS

Exposure to ≥ 2, and especially ≥ 4 consecutive night shifts associated with an increased likelihood of short sickness absence (OR 1.24, 95%CI 1.12-1.38 and OR 1.54, 95%CI 1.10-2.15, respectively) among shift workers, whereas a high number (> 25%) of evening shifts and having ≥ 2 consecutive evening shifts associated with lower odds of sickness absence. Over 40-hour weeks (OR 1.29, 95%CI 1.20-1.39), over 48-hour weeks (OR 1.24, 95%CI 1.01-1.54) and quick returns (OR 1.02, 95%CI 1.01-1.02) increased the likelihood of sickness absence. Having only a few (0-1 or 2-3) quick returns over 28 days decreased the likelihood of sickness absence (ORs 0.46 and 0.67), whereas having ≥ 5 quick returns over 28 days increased it (OR 1.88, 95%CI 1.76-1.99). These associations were similar among both shift and day workers.

CONCLUSIONS

Long working hours, several consecutive night shifts and quick returns increase the risk of short sickness absence. These working-hour characteristics should be avoided in shift scheduling to minimize sickness absence and increased costs for the employer.

Time course of recovery after two successive night shifts: A diary study among Austrian nurses

AIM

To explore how nurses recover from night shift work during periods off duty.

BACKGROUND

Given the large number of affected individuals globally, detrimental health outcomes of night shift work are an important public health issue. Sufficient recovery opportunities are essential to prevent prolonged fatigue associated with demanding tasks and high workload as experienced in nurses working long shifts during the night.

METHOD

Nurses (n = 53, 96% females) who worked in two public nursing homes in Austria completed a 5-day diary to collect data on well-being conceptualized by fatigue, distress and vigour.

RESULTS

Nurses experienced worse well-being post night shift days than during rest days. Well-being improved from post night shift day 2 to the following rest day 1 and continued improvement from rest day 1 to rest day 2.

CONCLUSIONS

Nurses who work at nights are at risk for experiencing prolonged fatigue. Our results suggest that after two consecutive 12-hr night shifts full recovery needs at least three days off work.

IMPLICATIONS FOR NURSING MANAGEMENT

Strategies for maintaining nurses' good health and caring attitudes as well as vigilance for patient safety should include fatigue management plans and optimised schedules for night shift work.