Differences in access to wage replacement benefits for absences due to work‐related injury or illness in Canada
Bibliographic record
Abstract
BACKGROUND: The objective of this article is to examine the factors associated with differences in access to income replacement benefits for workers experiencing a work-related injury or illness of 1-week or longer in the Canadian labor force. METHODS: This study utilized data from the Survey of Labour and Income Dynamics, a representative longitudinal survey conducted by Statistics Canada. A total of 3,352 work-related absences were identified. Logistic regression models examined factors at the individual, occupational, and geographic level that were associated with the probability of receiving compensation. RESULTS: The probability of not receiving employer or workers' compensation benefits was higher among women, immigrants in their first 10 years in Canada, younger workers, respondents who were in their first year of a job, those who were not members of a union or collective bargaining agreement, and part-time workers. CONCLUSIONS: More research is required to understand why almost 50% of respondents with 1-week or longer work-related absences did not report receiving workers' compensation payments following their absence. More importantly, research is required to understand why particular groups of workers are more likely to be excluded from any type of compensation for lost earnings after a work-related injury and illness in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".