Employers’ Perceived Career Impact of Canada’s Parental-Leave Extension from 35 to 61 Weeks—“An Empty Gift”
Bibliographic record
Abstract
Introduced in 1990, Canadian parental-leave policy has seen several iterations. The most recent policy change, introduced in December 2017, extended parental leave from 35 to 61 weeks, resulting in longer work interruptions. Forty-six structured interviews were conducted to explore Canadian employers’ perception of how use of the new extended leave may impact employees’ careers. Though some employers offered explicit support for employees, a large proportion of employers felt that use of the longer leave would negatively impact employees’ careers. The presence of unions appeared to insulate employees from a career impact. A thematic analysis revealed that the career impact perceived by employers resulted from concern for employees’ missed opportunities (e.g., training, promotions), length of absence, specific employment situations (e.g., role, level in the organization, career ambitions, and tenure with the organization), and gendered views of employee leave use. Given that the vast majority of Canadian parental-leave users continue to be women, this research highlights the presence of considerable workplace stigma for work interruptions and that longer parental leave may only serve to exacerbate that stigma, especially for women. Recommendations and implications for parental-leave policy, workers, and employers are discussed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".