The experiences of workers who do not successfully return to work following a work-related injury
Bibliographic record
Abstract
BACKGROUND: A minority of workers with work-related injuries experience challenges returning to work. While factors that hinder return-to-work (RTW) are well-documented, the consequences of failing to successfully return to work on the lives of workers who have experienced a workplace injury remain poorly understood. OBJECTIVE: The purpose of this study is to explore the experiences of workers who do not successfully return to work following a work-related injury. METHODS: Using an interpretive approach to qualitative research and maximal variability sampling, 11 workers who have sustained work-related injuries without a successful RTW and four service providers were recruited through community organizations. Participants were interviewed using a semi-structured interview guide. A constant comparative approach was used to identify key themes across the worker and service provider experiences. RESULTS: The findings that emerged from the analysis capture the challenging RTW experiences of workers and describe wide ranging impacts on their lives when their workers' compensation claims are denied or discontinued, including ongoing financial strain, family tensions, subsequent health concerns, and negative employment experiences. The findings also highlight the negative consequences of existing cost-cutting frameworks that can restrict entitlement and benefits for many people with disabilities. CONCLUSIONS: The findings from this study highlight the experiences of workers who might need additional supports throughout the RTW process, and begin to shed light on the impact on their lives when RTW is not successful.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".