Insurers’ perspective on barriers and facilitators for return to work after occupational injuries
Bibliographic record
Abstract
BACKGROUND: Despite the importance of the legislative and insurance systems in the return to work process after an occupational injury, the perspective of the insurer on what influences return to work has rarely been documented. OBJECTIVE: To understand the barriers or facilitators for return to work, from the perspective of the insurer. METHODS: A comprehensive qualitative approach was used. Semi-directed interviews were done with nineteen (19) insurers (claims adjudicator and rehabilitation case manager) from a Canadian workers' compensation board. A thematic analysis was done using QDA Minor Software. RESULTS: Fourteen themes (e.g. family reaction, quality of work relationship) were classified into four categories representing the main stakeholders: worker with disability, workplace, healthcare system and compensation system. Emotional, cognitive, and adaptive reactions from the worker and his family were identified. We observed that good work relations and support practices, lack of access to medical resources, focus on the employee's ability, and complexity and consequences of the compensation process are the main barriers and facilitators from the insurers' perspective. Many of the perceived elements are coherent with the compensation system's administrative and legal context. CONCLUSIONS: The results enable us to better understand the insurers' perspective regarding what influences return to work. It reinforces the necessity to consider the administrative and legal context to better understand the insurers' perspective.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".