A structured approach to effective mentoring of women health researchers in Africa
Bibliographic record
Abstract
Objectives: To formatively evaluate the HIGHER Women consortium's Mentor Protégée Program (MPP) and derive lessons for successful African women scientist mentorship. Design: Desk review of program documents and cross-sectional surveys of mentors and protégées. Setting: All 10 regions of Cameroon. Participants: Women working in health research participating in the MPP. Interventions: Building health research skills and providing support for women to cope within the African psycho-social environment using a holistic approach. Main outcome measures: Formed mentor-protégés duos applying the MPP with measurable accomplishments. Results: The consortium counted 121 members with 103 protégées and 18 mentors. Of 103 protégées, 35 responded to the 2018 survey, while 77 responded to the 2022 survey. Mentioned benefits of the program included an increase in scientific peer-reviewed journal publications and presentations at national and international conferences. In the 2022 survey, a Pearson correlation showed an r of 0.41, which, although not statistically significant (p = .592), suggests a positive correlation between the increased number of peer-reviewed articles and increased number of years as HIGHER Women protégées. Conclusions: Mentorship programs can help over time to bridge the gender gaps within Africa as well as the gaps between African-led research and the rest of the world while making a meaningful contribution to enhancing the quality, diversity, and productivity of researchers. A mentoring program such as the HIGHER Women MPP can be improved by leveraging local and international partners to foster the mentoring program's sustainability, scalability, and expanded reach. Funding: World Health Organization's Special Programme for Research and Training in Tropical Diseases (WHO/TDR) and Canada's International Development Research Centre (IDRC Canada).
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".