Medical Students’ Perspectives on Family Planning and Impact on Specialty Choice
Bibliographic record
Abstract
Importance: Physicians are known to delay childbearing compared with nonphysicians and to experience higher rates of age-related pregnancy complications. Delay of childbearing is more pronounced in surgical specialties, and family planning and building goals may influence specialty choice. Objective: To assess medical students' perspectives on the development of family planning goals and the timing of family building within a medical career to elucidate how these perceptions impact their choice of specialty. Design, Setting, and Participants: This qualitative study included fourth-year medical students at the University of Toronto Temerty Faculty of Medicine, Toronto, Ontario, Canada, and was conducted between May and August 2021. Participants were purposively sampled to maximize diversity of gender and specialty choice. Interviews were conducted via videoconferencing software that were recorded, transcribed verbatim, and verified for accuracy. Thematic analysis was completed independently by 2 researchers and consensus on final themes was reached through discussion among study investigators. Data were analyzed between September and December 2021. Main Outcomes and Measures: Participants were asked to share their perceptions of personal family planning goals, support currently in place, family planning education in medicine and factors contributing to their choice of specialty and program. Thematic analysis was completed. Results: A total of 34 fourth-year medical students (median [range] age, 26 [24-33] years; 23 females [67.6%]) were interviewed. Four main themes were identified: (1) there is no ideal time to family build in a medical career, (2) family planning is a taboo topic, (3) surgical specialties offer less support for family building, and (4) residents who have children are perceived to place a burden on their colleagues. Medical students considered their family planning while deliberating among specialty choices and their experiences were highly influential in shaping their specialty selection. Conclusions and Relevance: Results of this qualitative study suggest that medical students perceive that family building during training may have unfavorable implications for team dynamics and relationships with colleagues, and these perceptions may affect specialty choice and family planning goals. Integration of family planning discussions and support for family building into medical curricula is needed along with efforts to improve culture by supporting team dynamics and workload when students take parental leave.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".