Parental leave: from perception to first‐hand experience
Bibliographic record
Abstract
Purpose Paid parental leave for both mothers and fathers has fed countless debates. Four years after the implementation of a new parental leave policy in Quebec, this paper aims to assess how parental leave is perceived in the workplace. Design/methodology/approach Using data from employee surveys carried out in a municipal police service, the paper employs analysis of variance techniques to compare the perception of parental leave within two groups of respondents: those who had gone on parental leave and those who had not. Findings The findings highlight significant differences between the perceptions of parental leave entertained by the respondents who have taken it up and those who have not yet experienced parental leave. Social implications Analysing these differences has produced extremely interesting findings: adopting a public policy is not sufficient; organisations need to make employees feel supported in taking parental leave if they really want the policy to achieve the targeted results. Originality/value Paid parental leave is relatively new in Europe and almost non‐existent in North America and few studies have been carried out to measure their perception in the workplace. This research shows how important it is to follow the use of the policy to make sure that it does not have negative impacts for those who use it.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".