The feminization of medicine in Latin America: ‘More-the-merrier’ will not beget gender equity or strengthen health systems
Bibliographic record
Abstract
This viewpoint addresses the lack of gender diversity in medical leadership in Latin America and the gap in evidence on gender dimensions of the health workforce. While Latin America has experienced a dramatic change in the gender demographic of the medical field, the health sector employment pipeline is rife with entrenched and systemic gender inequities that continue to perpetuate a devaluation of women; ultimately resulting in an under-representation of women in medical leadership. Using data available in the public domain, we describe and critique the trajectory of women in medicine and characterize the magnitude of gender inequity in health system leadership over time and across the region, drawing on historical data from Mexico as an illustrative case. We propose recommendations that stand to disrupt the status quo to more appropriately value women and their representation at the highest levels of decision making for health. We call for adequate measurement of equity in medical leadership as a matter of national, regional, and global priority and propose the establishment of a regional observatory to monitor and evaluate meaningful progress towards gender parity in the health sector as well as in medical leadership.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".