Facilitating Working Mothers’ Ability to Breastfeed
Bibliographic record
Abstract
BACKGROUND: Mothers who work away from home tend to stop breastfeeding earlier than their nonworking counterparts due to workplace barriers. Barriers to breastfeeding discriminate against women and may lead to inequities in children's health outcomes. Guaranteeing paid breastfeeding breaks at work is 1 mechanism that can improve mothers' opportunity to breastfeed in the workplace. OBJECTIVE: This study aimed to assess the trends in the share of countries guaranteeing breastfeeding breaks in the workplace and paid maternal leave that lasts until the infant is 6 months old (the World Health Organization recommended duration for exclusive breastfeeding), between 1995 and 2014. METHODS: Legislation and secondary source data were collected and reviewed for 193 United Nations member states. Legislation was analyzed for content on breastfeeding breaks and maternal leave guarantees. RESULTS: Fifty-one countries (26.7%) in 2014 did not guarantee breastfeeding breaks in any form and 4 countries provided only unpaid breaks or breaks that did not cover the first 6 months of life; since 1995, around 15 countries (10.2%) legislated for such a policy. In 2014, out of 55 countries that did not guarantee paid breastfeeding breaks for the first 6 months after birth, 7 countries guaranteed paid maternal leave for the same duration; 48 countries (25.1%) provided neither paid maternal leave nor paid breastfeeding breaks. CONCLUSION: Progress in the number of countries guaranteeing breastfeeding breaks at work is modest. Adopting measures to facilitate breastfeeding at work can be a critical opportunity for countries to increase breastfeeding rates among the growing number of women in the labor force.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".