Comparing barriers and enablers of women’s health leadership in India with East Africa and North America
Bibliographic record
Abstract
Background: Women are estimated to hold between 70 and 75% of global health positions worldwide yet persistent inequities in power and leadership remain. There is little information on specific enablers and barriers that women working in public health face in India and how those compare with other regions. Methods: We collected and analyzed information from women working in public health in India and East Africa (Kenya, Rwanda, and Uganda) and in global health (Canada and United States), to understand and document the specific enablers and barriers women face in India, compared with other regions. Findings: Several universal themes emerged around factors enabling (mentors, professional networks, leadership based in empathy and team building) or impeding (obvert bias and family responsibilities) women across all contexts. Within this, there are nuances in how women's leadership growth factors and obstacles play out in India differently than in other contexts. Interpretation: There are important similarities in the enablers and barriers faced by women in India and other geographies and important ways these differs in for women in India. By designing programs and policies at institutional levels to address these factors, we can create a professional ecosystem that works for women in health and beyond. Funding: This research was funded by WomenLift Health, which is funded by the Bill and Melinda Gates Foundation. Representatives from WomenLift Health, listed as authors, participated in the conceptualization of the research to define objectives and core questions, provided commentary and revision to improve the manuscript, and supervised the progress of the research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| 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".