Mentoring, Training and Support to Global Health Innovators: A Scoping Review
Bibliographic record
Abstract
Global health innovators must navigate substantial complexities to successfully develop, implement and sustain global health innovations with impact through application of an Integrated InnovationTM approach. We sought to examine the nature of the literature and evidence around mentoring, training and support of global health innovators. We conducted a scoping review searching eight databases with terms capturing different kinds of innovation and support. Assessment of relevance and mapping was completed by two reviewers, with interpretation by the review team. Twenty-eight relevant papers provided perspectives on fostering global health innovators and innovation. Fifteen included empirical data on supports to global health innovators involving a wide range of innovators. Eight included documentation of outcomes but without designs to determine effectiveness. The diverse mentoring, training and support activities included: business incubators, support organizations and centres for entrepreneurship, technology transfer and intellectual property management, internship programs for business skill development, initiatives to bridge industry and researchers, and platforms for South-led innovation for global health. We propose the cultivation of a pipeline of global health innovators to increase the number of appropriate, sustainable innovations with impact in global health. Further empirical work on how to effectively support global health innovators is needed.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".