Determinants of duration of disability and return‐to‐work after work‐related injury and illness: Challenges for future research
Bibliographic record
Abstract
BACKGROUND: The purpose of this review was to identify critical data and research needs in addressing the following question: What are the primary factors that affect the time lost from work, return-to-work (RTW), subsequent unemployment, and changes in occupation after disabling illness or injury? METHODS: Review of the literature to identify research challenges originating from the multitude of disciplines, data sources, outcome measures, and methodological and analytical problems. RESULTS: About 100 different determinants of RTW outcomes were identified. Their impact varies across different phases of the disablement process. Recommendations are provided for addressing five selected research challenges. CONCLUSION: Interdisciplinary research needs to develop a comprehensive conceptual framework. Priority should be given to studies on specific domains of risk factors meeting five selection criteria: amenability to change; relevance to users of research; generalizability across health conditions, disability phases, and settings; "degree of promise" as derived from qualitative exploratory studies; and capacity to improve measurement instruments. Combining qualitative and quantitative research methods is necessary to bridge existing knowledge gaps.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".