Leading practices for men to support women’s health leadership: A toolkit of resources to initiate change
Bibliographic record
Abstract
Men have a critically important role to play in supporting women from different backgrounds to move into leadership roles. Indeed, it is necessary work for those in positions of privilege to challenge processes that result in inequitable gender outcomes in health leadership. We present the resources that have been compiled into a toolkit for men to support more inclusive health leadership and transformative systemic change. A three-step process was undertaken to search, select, and curate leading evidence-informed practices. Three key clusters of resources in the toolkit address why men's actions are necessary, what leading actions entail, and the importance of mentorship and sponsorship. Change will require more than shaping the individual attitudes and behaviours of men in leadership positions. Attention to gender and other forms of inequity need to be embedded into the structures, processes and outcomes of teams, organizations, and systems and evaluated for process.
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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 | 0.051 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".