Motherhood during residency training: challenges and strategies.
Bibliographic record
Abstract
OBJECTIVE: To determine what factors enable or impede women in a Canadian family medicine residency program from combining motherhood with residency training. To determine how policies can support these women, given that in recent decades the number of female family medicine residents has increased. DESIGN: Qualitative study using in-person interviews. SETTING: McMaster University Family Medicine Residency Program. PARTICIPANTS: Twenty-one of 27 family medicine residents taking maternity leave between 1994 and 1999. METHOD: Semistructured interviews. The research team reviewed transcripts of audiotaped interviews for emerging themes; consensus was reached on content and meaning. NVIVO software was used for data analysis. MAIN FINDINGS: Long hours, unpredictable work demands, guilt because absences from work increase workload for colleagues, and residents' high expectations of themselves cause pregnant residents severe stress. This stress continues upon return to work; finding adequate child care is an added stress. Residents report receiving less support from colleagues and supervisors upon return to work; they associate this with no longer being visibly pregnant. Physically demanding training rotations put additional strain on pregnant residents and those newly returned to work. Flexibility in scheduling rotations can help accommodate needs at home. Providing breaks, privacy, and refrigerators at work can help maintain breastfeeding. Allowing residents to remain involved in academic and clinical work during maternity leave helps maintain clinical skills, build new knowledge, and promote peer support. CONCLUSION: Pregnancy during residency training is common and becoming more common. Training programs can successfully enhance the experience of motherhood during residency by providing flexibility at work to facilitate a healthy balance among the competing demands of family, work, and student life.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 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".