Barriers to access maternity leave: A systematic narrative review
Bibliographic record
Abstract
Background The lack of access to comprehensive maternity leave could detrimentally affect mothers’ physical and mental health. Despite having the right to maternity leave, working mothers face several barriers to accessing it. These barriers to accessing maternity leave have not yet been systematised. Aim To identify the barriers to fully exercising the right to maternity leave. Methods We conducted a narrative synthesis, searching five databases and identifying 14,469 articles, from which we included 65 articles. We used the Newcastle-Ottawa Scale for cross-sectional studies, the Effective Public Healthcare Panacea Project Quality Assessment Tool for quantitative studies with other research designs, and the Critical Appraisal Skills Programme for qualitative studies. Findings Barriers include insufficient safeguards for implementing maternity leave policies in workplaces, hostile work environments that expose women to negotiating maternity leave or experiencing negative attitudes from coworkers, and being part of less advantaged sociodemographic groups, which hinder adequate access to maternity leave. Conclusion Mothers experience various barriers to accessing maternity leave, and women in more vulnerable groups are the most affected. Governments and employers can promote initiatives to reduce these barriers.
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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 | 0.021 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".