Experiences of Mothers Who Breastfeed and Pump while in Medical School: A Qualitative Study
Bibliographic record
Abstract
Background: Breastfeeding offers significant health benefits to both infants and mothers, including long-term protection against chronic diseases. It is supported as a fundamental right by global health organizations. Despite the integration of breastfeeding education into medical curricula, supportive policies for medical students who are mothers are often lacking, potentially impacting their academic and personal well-being. The objective of this study was to investigate the existing barriers to motherhood and breastfeeding in undergraduate medical education in order to provide policy recommendations to better support this student demographic. Methods: This qualitative study employed constructivist grounded theory through semi-structured interviews with 17 Canadian medical students, residents, or fellows who were or became mothers during medical school. Participants were recruited via social media, and data was collected and analyzed using NVivo software, with iterative coding and theme development by the research team. Results: Three main themes emerged: inadequate lactation infrastructure, unfavorable power dynamics affecting accommodation requests, and internal and external pressures influencing breastfeeding and career decisions. Participants highlighted significant challenges, including the lack of accessible lactation spaces, inconsistent policies, and stress related to advocating for breastfeeding needs. Conclusion: This study reveals a disparity between the teachings on breastfeeding importance in medical education and the practical support provided to breastfeeding students. Formalized, transparent policies are needed to support lactating medical students, ensure equity, and align institutional practices with educational objectives. Recommendations include designated lactation spaces, flexible scheduling, and standardized policies to foster an inclusive learning environment for all students.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".