Mentoring health researchers globally: Diverse experiences, programmes, challenges and responses
Bibliographic record
Abstract
Mentoring experiences and programmes are becoming increasingly recognised as important by those engaged in capacity strengthening in global health research. Using a primarily qualitative study design, we studied three experiences of mentorship and eight mentorship programmes for early career global health researchers based in high-income and low- and middle-income countries. For the latter, we drew upon programme materials, existing unpublished data and more formal mixed-method evaluations, supplemented by individual email questionnaire responses. Research team members wrote stories, and the team assembled and analysed them for key themes. Across the diverse experiences and programmes, key emergent themes included: great mentors inspire others in an inter-generational cascade, mentorship is transformative in personal and professional development and involves reciprocity, and finding the right balance in mentoring relationships and programmes includes responding creatively to failure. Among the challenges encountered were: struggling for more level playing fields for new health researchers globally, changing mindsets in institutions that do not have a culture of mentorship and building collaboration not competition. Mentoring networks spanning institutions and countries using multiple virtual and face-to-face methods are a potential avenue for fostering organisational cultures supporting quality mentorship in global health research.
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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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.005 |
| 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".