Union, employer and compensation system gaps and failures: Workers with injuries perceptions
Bibliographic record
Abstract
BACKGROUND: Workers who suffered a workplace injury and submitted a claim with the compensation board in Ontario often faced economic and non-economic costs that provoked depressive feelings, family strain, financial strain, and feelings of diminished self-worth. OBJECTIVE: This qualitative descriptive study aimed to understand the perceived gaps and failures associated with the support systems (e.g., union, compensation and employer) that were in place to assist some male underground workers in Sudbury, Ontario, Canada, who had suffered a workplace injury and had a compensation claim. METHODS: Twelve in-depth, in-person, individual, semi-structured interviews were conducted and data were transcribed verbatim and anonymized at the time of transcription. Data analysis followed Braun and Clarke's guidelines for thematic analysis. RESULTS: Themes that emerged include: unfair and inadequate recognition of an injury; limited communication with stakeholders involved with their claim, including claim adjudicators, challenges when returning to work, and compensation claim system barriers. CONCLUSIONS: Cooperation, collaboration, knowledge transfer, and decreased power imbalances could help to reduce the economic and non-economic strain felt by a worker with an injury. Additionally, a government-funded third-party advocate who knows the medical system, union contracts, the workers' compensation system, and employer policies and practices could act on behalf of an injured worker.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".