The Equity Tool for Valuing Global Health Partnerships
Bibliographic record
Abstract
Global health partnerships (GHPs) involve complex relationships between individuals and organizations, often joining partners from high-income and low- or middle-income countries around work that is carried out in the latter. Therefore, GHPs are situated in the context of global inequities and their underlying sociopolitical and historical causes, such as colonization. Equity is a core principle that should guide GHPs from start to end. How equity is embedded and nurtured throughout a partnership has remained a constant challenge. We have developed a user-friendly tool for valuing a GHP throughout its lifespan using an equity lens. The development of the EQT was informed by 5 distinct elements: a scoping review of scientific published peer-reviewed literature; an online survey and follow-up telephone interviews; workshops in Canada, Burkina Faso, and Vietnam; a critical interpretive synthesis; and a content validation exercise. Findings suggest GHPs generate experiences of equity or inequity yet provide little guidance on how to identify and respond to these experiences. The EQT can guide people involved in partnering to consider the equity implications of all their actions, from inception, through implementation and completion of a partnership. When used to guide reflective dialogue with a clear intention to advance equity in and through partnering, this tool offers a new approach to valuing global health partnerships. Global health practitioners, among others, can apply the EQT in their partnerships to learning together about how to cultivate equity in their unique contexts within what is becoming an increasingly diverse, vibrant, and responsive global health community.
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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 | 0.041 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".