Women physicians as healthcare leaders: a qualitative study
Bibliographic record
Abstract
Purpose - The purpose of this paper is to explore the under-representation of women physicians in clinical leadership by examining the issue from their perspective. Design/methodology/approach - The authors used large group engagement methods to explore the experiences and perceptions of women physicians. In order to capture common themes across this group as a whole, participants were selected using purposeful sampling. Data were analysed using a structured thematic analysis procedure. Findings - This paper provides empirical insights into the influences affecting women physicians' decision to participate in leadership. The authors found that they often exclude themselves because the costs of leadership outweigh the benefits. Potential barriers unique to healthcare include the undervaluing of leadership by physician peers and perceived lack of support by nursing. Research limitations/implications - This study provides an in-depth examination of why women physicians are under-represented in clinical leadership from the perspective of those directly involved. Further studies are needed to confirm the generalizability of these findings and potential differences between demographic groups of physicians. Practical implications - Healthcare organizations seeking to increase the participation of women physicians in leadership should focus on modifying the perceived costs of leadership and highlighting the potential benefits. Large group engagement methods can be an effective approach to engage physicians on specific issues and mobilize grass-roots support for change. Originality/value - This exploratory study provides insights on the barriers and enablers to leadership specific to women physicians in the clinical setting. It provides a reference for healthcare organizations seeking to develop and diversify their leadership talent.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".