Which interventions are used in the rehabilitation, return, and stay at work process of aging workers having suffered an occupational injury? A scoping review
Bibliographic record
Abstract
BACKGROUND: Considering current labour shortages, the recent increase in the number of aging workers in the labour market is deemed economically beneficial. However, due to specific characteristics (e.g. biological, generational), aging workers take longer to recover and return to work after an occupational injury. Yet, few studies have examined the rehabilitation, return, and stay at work process of aging workers and current knowledge makes it difficult for stakeholders to identify which interventions to use with this specific population. OBJECTIVE: This study aimed to describe which interventions are used in the rehabilitation, return, and stay at work process of aging workers having suffered an occupational injury. METHODS: We conducted a five-step scoping review. Five databases were used for the literature search. A qualitative analysis of the retained manuscripts was conducted. RESULTS: Our analysis led to the extraction of information from seven manuscripts that concern the rehabilitation, return, and stay at work process of aging workers who suffered an occupational injury exclusively. We identified 19 interventions (e.g. work capacity development, work task modifications, permanent part-time work, and alternative roles) and were described according to the different phases of the Cycle of Work Disability Prevention (CWDP). CONCLUSIONS: This study offers informative, non-prescriptive, and operational interventions useful for stakeholders who support aging workers. Furthermore, it is a knowledge base to develop future projects that promote the rehabilitation, return, and stay at work process of aging workers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".