Women in medicine: the challenge of finding balance.
Bibliographic record
Abstract
OBJECTIVE: To examine the experiences of women physicians with regard to the interplay between career and lifestyle choices and to discover how women's experiences have evolved during the past 3 decades. DESIGN: Qualitative study using a phenomenologic approach and in-depth interviews. SETTING: Southwestern Ontario. PARTICIPANTS: A total of 12 women physicians. METHOD: A purposeful sample of women physicians was selected using a maximum variation sampling strategy. Through semistructured interviews, participants' experiences, opinions, behaviour, and feelings were explored. All interviews were audiotaped and transcribed. The analysis strategy was both iterative and interpretive. Researchers independently reviewed and coded each transcript to identify key emerging themes, and the research team met to discuss and compare individual interpretations. Interviews continued until saturation was achieved. MAIN FINDINGS: Three main challenges emerged from the women physicians' comments: lifestyle and career choices, family planning and career trajectory, and seeking balance. CONCLUSION: Despite the increased number of women physicians in the work force, the experiences and challenges faced by these women have not evolved during the past 30 years. Women continue to experience the strain of their dual role as women and as physicians, discordance between career and lifestyle choices, and difficulties with timing pregnancies. Some changes in legislation have been made to benefit women physicians, but these changes have not yet influenced attitudes and behaviour in the workplace.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| 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".