Methodological approach for measuring the effects of organisational-level interventions on employee withdrawal behaviour

• 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Annan hälsovetenskap (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Other Health Sciences (hsv//eng)

SAMHÄLLSVETENSKAP  -- Psykologi -- Tillämpad psykologi (hsv//swe)

SOCIAL SCIENCES  -- Psychology -- Applied Psychology (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Arbetsterapi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Occupational Therapy (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Arbetsmedicin och miljömedicin (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Occupational Health and Environmental Health (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)

Nyckelord

Work environment

Time series analysis

Sickness absence

Mixed-effects models

Workplace interventions

Public sector

Organisation

Employee turnover

Organisational-level intervention

Process evaluation

Employee turnover

Mixed-effects models

Organisation

Organisational-level intervention

Process evaluation

Public sector

Sickness absence

Time series analysis

Work environment

Workplace interventions

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