Examining representation of women in leadership of professional medical associations in India
Bibliographic record
Abstract
Women constitute 70% of the global health workforce but are significantly underrepresented in leadership positions. In India, professional medical associations (PMAs) play a crucial role in shaping policy agenda in the health sector, but very little is known about gender diversity in their leadership. Therefore, we analysed the gender representation of current and past leaderships of Indian PMAs. Data of the current and past national leadership and leadership committees of 46 leading PMAs representing general, specialities, and super-specialities were extracted from their official websites. Gender composition of leadership was analysed using a sequential approach. For Indian Medical Association (IMA), the largest Indian PMA, an analysis of its 32 sub-chapters was also undertaken. The findings revealed that only 9 (19.5%) out of 46 associations are currently led by a woman. Leadership committees of half the associations have less than 20% women, while there were no women in the central committee of nine PMAs. Among past presidents, information was publicly available for 31 associations and all of them have had less than 20% of women presidents till date. Among the 64 individuals currently serving as presidents and secretaries of 32 sub-chapters of IMA, only three (4.6%) are women. Even in associations closely related to women's health, such as obstetrics and gynecology, pediatrics, and neonatology, unequal representation persists, highlighting male dominance. These results demonstrate significant gender disparities in PMA leadership in India, necessitating urgent efforts to promote gender equality. Gender-transformative leadership is crucial to develop gender-sensitive health care policies and practices which can serve as a catalyst for broader societal change.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".