Racial and Ethnic Inequities in the Return-to-Work of Workers Experiencing Injury or Illness: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Non-White workers face more frequent, severe, and disabling occupational and non-occupational injuries and illnesses when compared to White workers. It is unclear whether the return-to-work (RTW) process following injury or illness differs according to race or ethnicity. OBJECTIVE: To determine racial and ethnic differences in the RTW process of workers with an occupational or non-occupational injury or illness. METHODS: A systematic review was conducted. Eight academic databases - Medline, Embase, PsycINFO, CINAHL, Sociological Abstracts, ASSIA, ABI Inform, and Econ lit - were searched. Titles/abstracts and full texts of articles were reviewed for eligibility; relevant articles were appraised for methodological quality. A best evidence synthesis was applied to determine key findings and generate recommendations based on an assessment of the quality, quantity, and consistency of evidence. RESULTS: 15,289 articles were identified from which 19 studies met eligibility criteria and were appraised as medium-to-high methodological quality. Fifteen studies focused on workers with a non-occupational injury or illness and only four focused on workers with an occupational injury or illness. There was strong evidence indicating that non-White and racial/ethnic minority workers were less likely to RTW following a non-occupational injury or illness when compared to White or racial/ethnic majority workers. CONCLUSIONS: Policy and programmatic attention should be directed towards addressing racism and discrimination faced by non-White and racial/ethnic minority workers in the RTW process. Our research also underscores the importance of enhancing the measurement and examination of race and ethnicity in the field of work disability management.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".