Gender-based differences in the representation and experiences of academic leaders in medicine and dentistry: a mixed method study from Pakistan
Bibliographic record
Abstract
BACKGROUND: Research evidence suggests gender-based differences in the extent and experiences of academic leaders across the globe even in developed countries like USA, UK, and Canada. The under-representation is particularly common in higher education organizations, including medical and dental schools. The current study aimed to investigate gender-based distribution and explore leaders' experiences in the medical and dental institutes in a developing country, Pakistan. METHODS: A mixed-method approach was used. Gender-based distribution data of academic leaders in 28 colleges including 18 medical and 10 dental colleges of Khyber Pakhtunkhwa, Pakistan were collected. Qualitative data regarding the experiences of academic leaders (n = 10) was collected through semi-structured interviews followed by transcription and thematic analysis using standard procedures. RESULTS: Gender-based disparities exist across all institutes with the greatest differences among the top-rank leadership level (principals/deans) where 84.5% of the positions were occupied by males. The gender gap was relatively narrow at mid-level leadership positions reaching up to as high as > 40% of female leaders in medical/dental education. The qualitative analysis found gender-based differences in the experiences under four themes: leadership attributes, leadership journey, challenges, and support. CONCLUSIONS: The study showed that women are not only significantly under-represented in leadership positions in medical and dental colleges in Pakistan, they also face gender-based discrimination and struggling to maintain a decent work life balance. These findings are critical and can have important implications for government, organizations, human resource managers, and policymakers in terms of enacting laws, proposing regulations, and establishing support mechanisms to improve gender-based balance and help current and aspiring leaders in their leadership journey.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".