Using Dialogue to Address Jurisdictional Inequities in Access to Return to Work Resources and Identify Policy Weaknesses for Workers in Situations of Vulnerability
Bibliographic record
Abstract
In Canada, occupational health and safety (OHS) and workers' compensation are primarily provincial responsibilities and there is no national institute for OHS research. Research capacity and many civil society resources to which injured workers can turn for support are primarily concentrated in three provinces. Labor force composition, employment options, vulnerability to injury, and return to work (RTW) challenges vary across jurisdictions and are changing over time, but not at the same rate. When coupled with jurisdictional inequities in RTW research and civil society supports, these differences have the potential to contribute to policy gaps and situations where issues addressed in one jurisdiction emerge again in another. This article reports on a multi-stakeholder, virtual dialogue process designed to help identify and address these potential inequities by transferring research insights related to RTW for workers in situations of vulnerability (e.g., precarious employment) and findings from a comparative policy scan to Newfoundland and Labrador (NL), a province with very limited RTW research capacity and civil society supports for injured workers. We describe the context, the dialogue process, key results from the policy scan, and we reflect on the opportunities and constraints of these knowledge synthesis and exchange tools as vehicles to address jurisdictional disparities in RTW research, policy and supports for workers injured in precarious employment and other vulnerable situations in a context of economic and policy change.
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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 | 0.134 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.034 | 0.034 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.005 | 0.046 |
| Research integrity | 0.010 | 0.012 |
| 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".