Global Health Mentorship: Challenges and Opportunities for Equitable Partnership
Bibliographic record
Abstract
⇒ Mentorship in global health is a critical determinant of equitable, sustainable and inclusive improvements in health outcomes globally.⇒ Researchers from the Global South face unique global health mentorship challenges.Some of these challenges include: limited opportunities, access to mentorship opportunities, lack of a healthy mentorship culture, weak and insufficient institutional support, language barriers (non-English speakers) and colonial mentorship mindset.⇒ Healthy and respectful South-South and North-South collaborations and partnerships are needed.Indeed, ensuring ethical mentorship practices that respect and learn from cultural differences and integrate bidirectional North-South and South-North co-learning parallels are needed.⇒ A decolonised global health mentorship agenda is highly needed.Operationalising what ethical global health mentorship is, or should be, and how global health mentorship should be decolonised remain areas that deserve keen attention.
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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 | 0.122 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.031 | 0.029 |
| Open science | 0.005 | 0.045 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.037 | 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".