Male Allyship to Advance Women’s Global Health Leadership in the Academy
Bibliographic record
Abstract
Abstract Women are underrepresented in leadership positions within global health. Although women leaders have been shown to foster inclusive work environments and prioritize improvements in women’s health, they face barriers to their advancement, including microaggressions and disproportionate caregiving responsibilities. Male allyship can facilitate the elevation of women into global health leadership roles. This study explores the experiences of global health leaders in academia on male allyship and identifies actions and best practices to support the growth of women’s leadership in global health. Qualitative semi-structured interviews were conducted with twenty-one global health leaders (11 females, 10 males) from U.S. and Canadian academic institutions. Interviews were recorded, transcribed, and coded utilizing a combined inductive-deductive approach. Participants identified barriers and outlined potential approaches to support women’s advancement to leadership roles. For the individual male ally, recommendations included completing a self-assessment (to mitigate counterproductive behaviors and biases), engaging in effective mentorship practices, advocating publicly, and serving as a positive role model. Recommendations at the institutional level emphasize the importance of cultivating an enabling environment that facilitates open dialogue, establishing goals and metrics; and implementing allyship training with periodic evaluation. At the societal level, participants suggested promoting early education and shared caregiving to shift cultural norms on gender roles. This paper provides a framework of actions and resources to cultivate and support male allyship for women’s leadership advancement in global health. Effective male allyship begins with acknowledging power dynamics and an understanding of how intersectionality, beyond gender alone, shapes women’s careers and workplace dynamics. Additionally, mentorship and collaborative peer support are critical to promoting women’s career development. Individual allyship when combined with institutional and societal actions and policies, can facilitate the advancement of women in global health leadership roles.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".