Examining Medical Student Specialty Choice Through a Gender Lens: An Orientational Qualitative Study
Bibliographic record
Abstract
Phenomenon: A growing number of women are entering the medical workforce, yet their distribution across medical specialties remains nonuniform. We sought to describe how culture, bias, and socialization shape gendered thinking regarding specialty choice at a Canadian undergraduate medical institution. APPROACH: We analyzed transcripts from the Career Choices Project: 16 semistructured focus group discussions with 70 students graduating from Memorial University of Newfoundland in 2003, 2006, 2007, and 2008. The questions and prompts were designed to explore factors influencing specialty choice and did not specifically probe gender-based experiences. Focus groups were audio-recorded, transcribed, and deidentified before analysis. Analysis was inductive and guided by principles of orientational qualitative inquiry using a gender-specific lens. FINDINGS: The pursuits of personal and professional goals, as well as contextual factors, were the major themes that influenced decision-making for women and men. Composition of these major themes varied between genders. Influence of a partner, consideration of familial commitments (both present and future), feeling a sense of connectedness with the field in question, and social accountability were described by women as important. Both genders hoped to pursue careers that would afford "flexibility" in order to balance work with their personal lives, though the construct of work-life balance differed between genders. Women did not explicitly identify gender bias or sexism as influencing factors, but their narratives suggest that these elements were at play. Insights: Our findings suggest that unlike men, women's decision-making is informed by tension between personal and professional goals, likely related to the context of gendered personal and societal expectations.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".