Workplace involvement improves return to work rates among employees with back pain on long-term sick leave: a systematic review of the effectiveness and cost-effectiveness of interventions
Bibliographic record
Abstract
PURPOSE: Long-term sickness absence among workers is a major problem in industrialised countries. The aim of the review is to determine whether interventions involving the workplace are more effective and cost-effective at helping employees on sick leave return to work than those that do not involve the workplace at all. METHODS: A systematic review of controlled intervention studies and economic evaluations. Sixteen electronic databases and grey literature sources were searched, and reference and citation tracking was performed on included publications. A narrative synthesis was performed. RESULTS: Ten articles were found reporting nine trials from Europe and Canada, and four articles were found evaluating the cost-effectiveness of interventions. The population in eight trials suffered from back pain and related musculoskeletal conditions. Interventions involving employees, health practitioners and employers working together, to implement work modifications for the absentee, were more consistently effective than other interventions. Early intervention was also found to be effective. The majority of trials were of good or moderate quality. Economic evaluations indicated that interventions with a workplace component are likely to be more cost effective than those without. CONCLUSION: Stakeholder participation and work modification are more effective and cost effective at returning to work adults with musculoskeletal conditions than other workplace-linked interventions, including exercise.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".