Sequence analysis of the combinations of work shifts and absences in health care – comparison of two years of administrative data
Abstract 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 emplo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | BMC Nursing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12912-022-01160-1 |
| _version_ | 1797973704080097280 |
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| author | Oxana Krutova Laura Peutere Jenni Ervasti Mikko Härmä Marianna Virtanen Annina Ropponen |
| author_facet | Oxana Krutova Laura Peutere Jenni Ervasti Mikko Härmä Marianna Virtanen Annina Ropponen |
| author_sort | Oxana Krutova |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-11T04:08:33Z |
| format | Article |
| id | doaj.art-73609aa395cc43808ee01a39c0c8eb31 |
| institution | Directory Open Access Journal |
| issn | 1472-6955 |
| language | English |
| last_indexed | 2024-04-11T04:08:33Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj.art-73609aa395cc43808ee01a39c0c8eb312023-01-01T12:16:15ZengBMCBMC Nursing1472-69552022-12-0121111110.1186/s12912-022-01160-1Sequence analysis of the combinations of work shifts and absences in health care – comparison of two years of administrative dataOxana Krutova0Laura Peutere1Jenni Ervasti2Mikko Härmä3Marianna Virtanen4Annina Ropponen5Finnish Institute of Occupational HealthSchool of Educational Sciences and Psychology, University of Eastern FinlandFinnish Institute of Occupational HealthFinnish Institute of Occupational HealthSchool of Educational Sciences and Psychology, University of Eastern FinlandFinnish Institute of Occupational HealthAbstract 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.https://doi.org/10.1186/s12912-022-01160-1Shift workAbsenceSequence analysisLongitudinalHealth careEmployees |
| spellingShingle | Oxana Krutova Laura Peutere Jenni Ervasti Mikko Härmä Marianna Virtanen Annina Ropponen Sequence analysis of the combinations of work shifts and absences in health care – comparison of two years of administrative data BMC Nursing Shift work Absence Sequence analysis Longitudinal Health care Employees |
| title | Sequence analysis of the combinations of work shifts and absences in health care – comparison of two years of administrative data |
| title_full | Sequence analysis of the combinations of work shifts and absences in health care – comparison of two years of administrative data |
| title_fullStr | Sequence analysis of the combinations of work shifts and absences in health care – comparison of two years of administrative data |
| title_full_unstemmed | Sequence analysis of the combinations of work shifts and absences in health care – comparison of two years of administrative data |
| title_short | Sequence analysis of the combinations of work shifts and absences in health care – comparison of two years of administrative data |
| title_sort | sequence analysis of the combinations of work shifts and absences in health care comparison of two years of administrative data |
| topic | Shift work Absence Sequence analysis Longitudinal Health care Employees |
| url | https://doi.org/10.1186/s12912-022-01160-1 |
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