O-397 RETIREMENT PENSION POVERTY AMONG INJURED WORKERS WITH LONG TERM WORKERS’ COMPENSATION CLAIMS
Bibliographic record
Abstract
Abstract Background Sustaining sufficient income in retirement becomes particularly challenging for injured workers supported during their working years by workers’ compensation benefits. This study examined the income security of injured workers aged 65 and older in Ontario (Canada’s largest province) who had spent five years or more on workers’ compensation benefits. Methods Our 2019-2022 multi-method study involved: 1) scanning Canadian workers’ compensation policies on retirement age benefits; 2) reviewing Ontario parliament transcripts on political debates surrounding the injured worker pension benefit cuts; 3) in-depth interviews on workers’; experiences of workers’ compensation retirement benefits. Results Most Canadian workers’ compensation boards made significant cuts to retirement benefits, substantially reducing retired injured worker incomes. Monthly pension payments had shifted to lump-sum payouts, which disrupted workers’ access to social security supports. The political debates about cuts to injured worker retirement pensions focused mostly on affordability and concepts of worker deservingness. Interviews with injured workers revealed deep poverty of older injured workers. Discussion Workers’ compensation is intended to compensate for income losses related to work-related ill-health and injury, including losses to contributions to the Canadian pension retirement plan, but this is no longer provided in Canada and workers face related economic hardship. Conclusion Our study is one of the first to examine workers’ compensation benefits for injured workers in retirement. It draws attention to the problem of poverty among injured workers in their retirement years and raises questions about politics, reduced retirement benefits and adequacy of the Canadian social security net.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".