Informing ‘good’ global health research partnerships: A scoping review of guiding principles
Bibliographic record
Abstract
Background: Several sets of principles have been proposed to guide global health research partnerships and mitigate inequities inadvertently caused by them. The existence of multiple sets of principles poses a challenge for those seeking to critically engage with and develop their practice. Which of these is best to use, and why? To what extent, if any, is there agreement across proposed principles?Objective: The objectives of this review were to: (1) identify and consolidate existing documents and principles to guide global health research partnerships; (2) identify areas of overlapping consensus, if any, regarding which principles are fundamental in these partnerships; (3) identify any lack of consensus in the literature on core principles to support these partnerships.Methods: A scoping review was conducted to gather documents outlining ‘principles’ of good global health research partnerships. A broad search of academic databases to gather peerreviewed literature was conducted, complemented by a hand-search of key global health funding institutions for grey literature guidelines.Results: Our search yielded nine sets of principles designed to guide and support global health research partnerships. No single principle recurred across all documents reviewed. Most frequently cited were concerns with mutual benefits between partners (n = 6) and equity (n = 4). Despite a lack of consistency in the inclusion and definition of principles, all sources highlighted principles that identified attention to fairness, equity, or justice as an integral part of good global health research partnerships.Conclusions: Lack of consensus regarding how principles are defined suggests a need for further discussion on what global health researchers mean by ‘core’ principles. Research partnerships should seek to interpret the practical meanings and requirements of these principles through international consultation. Finally, a need exists for tools to assist with implementation of these principles to ensure their application in research practice.
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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 | 0.250 | 0.391 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.046 | 0.048 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.011 | 0.013 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".