Managers' psychosocial working conditions are important for managerial sustainability in the public sector. The job demands-resources (JD-R)
Background Theoretical frameworks have recommended organisational-level interventions to decrease employee withdrawal behaviours such as sickness absence and employee turnover. However, evaluation of such interventions has produced inconclusive results. The aim of this study was to investigate if mixed-effects models in combination with time series analysis, process evaluation, and reference group comparisons could be used for evaluating the effects of an organisational-level intervention on employee withdrawal behaviour. Methods Monthly data on employee withdrawal behaviours (sickness absence, employee turnover, employment rate, and unpaid leave) were collected for 58 consecutive months (before and after the intervention) for intervention and reference groups. In total, eight intervention groups with a total of 1600 employees participated in the intervention. Process evaluation data were collected by process facilitators from the intervention team. Overall intervention effects were assessed using mixed-effects models with an AR (1) covariance structure for the repeated measurements and time as fixed effect. Intervention effects for each intervention group were assessed using time series analysis. Finally, results were compared descriptively with data from process evaluation and reference groups to disentangle the organisational-level intervention effects from other simultaneous effects. Results All measures of employee withdrawal behaviour indicated statistically significant time trends and seasonal variability. Applying these methods to an organisational-level intervention resulted in an overall decrease in employee withdrawal behaviour. Meanwhile, the intervention effects varied greatly between intervention groups, highlighting the need to perform analyses at multiple levels to obtain a full understanding. Results also indicated that possible delayed intervention effects must be considered and that data from process evaluation and reference group comparisons were vital for disentangling the intervention effects from other simultaneous effects. Conclusions When analysing the effects of an intervention, time trends, seasonal variability, and other changes in the work environment must be considered. The use of mixed-effects models in combination with time series analysis, process evaluation, and reference groups is a promising way to improve the evaluation of organisational-level interventions that can easily be adopted by others.
Organisational-level interventions are recommended for decreasing sickness absence, but knowledge of the optimal design and implementation of such interventions is scarce. We collected data on working conditions, motivation, health, employee turnover, and sickness absence among participants in a large-scale organisational-level intervention comprising measures designed and implemented by line managers and their human resources partners (i.e., operational-level). Information regarding the process, including the implementation of measures, was retrieved from a separate process evaluation, and the intervention effects were investigated using mixed-effects models. Data from reference groups were used to separate the intervention effect from the effects of other concurrent changes at the workplace. Overall, working conditions and motivation improved during the study for both the intervention and reference groups, but an intervention effect was only seen for two of 13 evaluated survey items: clearness of objectives (p = 0.02) and motivation (p = 0.06). No changes were seen in employees’ perceived health, and there were no overall intervention effects on employee turnover or sickness absence. When using operational-level workplace interventions to improve working conditions and employees’ health, efforts must be made to achieve a high measure-to-challenge correspondence; that is, the implemented measures must be a good match to the problems that they are intended to address.
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