Understanding gendered experiences in academic health sciences: a grounded theory study on leadership and continuing professional development
Bibliographic record
Abstract
Introduction: In order to improve equity amongst leadership roles for all genders, it is important for health professions educators to better understand the intersection of gender and leadership. This study aimed to understand how gender affects leaders in health sciences and their engagement in developing themselves further within their career by exploring: 1) Their engagement with continuing professional development; 2) Their motivations for continued learning; 3) The benefits/consequences of their careers. Methods: A Constructivist Grounded Theory approach was used to investigate this domain. Eligible leaders in health sciences were invited for one-on-one virtual interviews that were transcribed and analyzed by our research team. The data were examined initially in a constant comparative method with reflexive journaling and subsequently examined through axial coding for further themes. Results: Eighteen qualitative interviews were analyzed. Themes pertaining to support systems, sponsorship/mentorship, and a lack of discourse surrounding gendered constraints were identified at the intersection between gender, academic leadership, and CPD within healthcare education. Conclusion: Our findings provide insight on the gender gap and its implications on healthcare leaders' motivations in their role, as well as engagement in continuing professional development.
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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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".