How gender shapes practice choices among family medicine residents and early career family physicians: a Canadian qualitative study
Bibliographic record
Abstract
BACKGROUND: The practice choices of family medicine residents and early career family physicians shape access to primary care. A growing proportion of family physicians are women. AIM: To examine how gender operates in shaping family physician practice choices and subsequent practice patterns. DESIGN & SETTING: Qualitative interview data were analysed. Family medicine residents and early career family physicians from three Canadian provinces (Nova Scotia, Ontario, and British Columbia) participated in interviews. METHOD: Qualitative interview data were collected as part of a larger mixed-methods study. Eighty-eight interviews were transcribed verbatim and coded into several node reports, including one on gender. Reflexive thematic analysis was conducted to identify themes related to how gender impacts physician practice choices. RESULTS: Many participants described multiple intersecting pathways through which it was apparent that gender shaped their career and practice choices. Others did not identify the impact of gender in this regard. Parenthood and caregiving were commonly discussed, as were clinical interests specific to women's health; however, gendered expectations of patients and colleagues were also seen to shape choices. In this way, gender shaped choices directly, but also indirectly in response to gendered experiences and expectations. CONCLUSION: Findings support the need for structural reforms including: increased availability of collaborative team-based models, flexible work schedules, closure of gendered wage gaps, and integration of gender awareness training through academic and healthcare institutions. Consideration of how primary care policies differentially impact across clinician gender is key to future planning to support a changing workforce that meets patient needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".