Leadership Gender Disparity Within Research-Intensive Medical Schools: A Transcontinental Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: The underrepresentation of women in senior leadership positions of academic medicine continues to prevail despite the ongoing efforts to advance gender parity. Our aim was to compare the extent of gender imbalance in the leadership of the top 100 medical schools and to critically analyze the contributing factors through a comprehensive theoretical framework. METHODS: We adopted the theoretical framework of the Systems and Career Influences Model. The leadership was classified into four tiers of leadership hierarchy. Variables of interest included gender, h-index, number of documents published, total number of citations, and number of years in active research. A total of 2448 (77.59%) men and 707 (22.41%) women met the inclusion criteria. RESULTS: Male majority was found in all regions with a significant difference in all levels of leadership (chi square = 91.66; P value = .001). Women had a lower mean h-index across all positions in all regions, and when we adjusted for number of years invested, M Index for women was still significantly lower than men (T test = 6.52; P value = .02). DISCUSSION: Organizational and individual influences are transcontinental within the top 100 medical school leadership hierarchy. Those factors were critically assessed through in-depth analysis of the Systems and Career Influences Model. Evidence-driven actionable recommendations to remedy those influences were outlined.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".