Determinants of duration of disability and return‐to‐work after work‐related injury and illness: Challenges for future research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it