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Barriers to access maternity leave: A systematic narrative review

2025· article· en· W4416225997 on OpenAlexaboutno aff
Liliana Hidalgo-Padilla, Mauricio Toyama, Adriana Carbonel, Jessica Hanae Zafra‐Tanaka, Alejandra Vives, Francisco Diez‐Canseco

Bibliographic record

VenueWellcome Open Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
FundersWellcome Trust
KeywordsPanacea (medicine)NegotiationMaternity careNarrativeScale (ratio)Work (physics)Narrative reviewQualitative researchCritical appraisal

Abstract

fetched live from OpenAlex

<ns3:p>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.</ns3:p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.772
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0050.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.183
GPT teacher head0.510
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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