An evaluation of the association between changes to job protection during illness leave and illness absence behaviour
Bibliographic record
Abstract
Abstract Background Despite a growing body of research on sick leave policies, there remains a significant gap in research concerning job protections during illness leaves, which is critical in Canada since several provinces are considering or passing job-protected leave expansions. We examined three major job-protected leave expansions, Quebec (2003), Manitoba (2016), and Alberta (2018). Methods We used the Canadian Labour Force Survey data spanning from 1998 to 2022. Using a difference-in-difference approach, we examined 5-year changes in leave behaviour before and after expansions in job-protected leave in the three provinces compared to changes in provinces with less than 2 weeks of job-protected leave. We analyzed the prevalence, duration, and distribution of illness/disability absences using ordinary least squares and linear probability models. Results We found that expanding job-protected leave in Quebec was associated with decreases in the overall length of leave by 2.2 (95% CI: -3.2 to -1.5; P<0.001) weeks or 14.0% relative reduction. Similarly, expansions in Alberta and Manitoba were associated with decreases in the overall length of leave by 1.2 (95% CI: -2.1 to -0.3; P=0.016) weeks or 7.4% relative reduction. Results for absence prevalence were mixed (small increase (Quebec) (p<0.05), no significant change (Alberta-Manitoba) (p>0.05). Both expansions were associated with significant increases in absence duration consistent with the policy (i.e., 3-17-week leaves) (p<0.05). Conclusions Our results suggest that job-protected leave expansion may influence leave behaviour even in the presence of protections provided by human rights laws and without imposing large additional costs for employers or governments.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".