Experiences, impacts and service needs of injured and ill workers in the WSIB process: evidence from Thunder Bay and District (Ontario, Canada)
Bibliographic record
Abstract
INTRODUCTION: Individuals experience negative physical, social and psychological ramifications when they are hurt or become ill at work. Ontario's Workplace Safety and Insurance Board (WSIB) is intended to mitigate these effects, yet the WSIB process can be difficult. Supports for injured workers can be fragmented and scarce, especially in underserved areas. We describe the experiences and mental health needs of injured and ill Northwestern Ontario workers in the WSIB process, in order to promote system improvements. METHODS: Community-recruited injured and ill workers (n = 40) from Thunder Bay and District completed an online survey about their mental health, social service and legal system needs while involved with WSIB. Additional Northwestern Ontario injured and ill workers (n = 16) and community service providers experienced with WSIB processes (n = 8) completed interviews addressing similar themes. RESULTS: Northwestern Ontario workers described the impacts of workplace injury and illness on their professional, family, financial and social functioning, and on their physical and mental health. Many also reported incremental negative impacts of the WSIB processes themselves, including regional issues such as "small town" privacy concerns and the cost burden of travel required by the WSIB, especially during COVID-19. Workers and service providers suggested streamlining and explicating WSIB processes, increasing WSIB continuity of care, and region-specific actions such as improving access to regional support services through arm's-length navigators. CONCLUSION: Northwestern Ontario workers experienced negative effects from workplace injuries and illness and the WSIB process itself. Stakeholders can use these findings to improve processes and outcomes for injured and ill workers, with special considerations for the North.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".