What works for whom in occupational physiotherapy: Supporting decisions for sustainable employability

Both workers and society benefit from sustainable employability: staying at work often contributes to mental and physical health as well as financial independence. At the same time, demographic changes, such as an aging workforce and rising retirement ages, requires to support sustainable employability. But what works for whom?
Much remains unknown about predicting sustainable employability, with other factors playing a role compared to those involved in reintegration or risk prevention. Six Dijkstra examined how personalized advice from occupational physiotherapists for sustainable employability can be improved within current approach. Her study focused on workability and vitality, measured during Occupational Health Check-ups (OHCs), and subsequent lifestyle advice. Despite the widespread use of OHCs in the Netherlands, their effectiveness remains unclear.
Six Dijkstra explored whether machine learning (ML) could identify complex patterns in OHC data and examined resilience as a potential predictor of employability. A key aim was to develop an ML-based decision support tool for occupational physiotherapists.
However, the models proved limited in predictive value, despite ten years of data. The study offers valuable insights for future research and highlights ethical considerations regarding the use of ML in occupational healthcare. The results indicate that resilience could serve as an early indicator of employability, though further validation is needed.
This research contributes to the knowledge on providing tailored advice and the responsible use of ML with existing databases. Policymakers, occupational physiotherapists, and researchers are encouraged to critically assess the use of OHCs and apply technologies like ML carefully in advising for sustainable employability.