Global health research case studies: lessons from partnerships addressing health inequities
Bibliographic record
Abstract
Inspiration for compiling this collection of case studies comes from the Global Health Research Initiative's (GHRI) commitment to conceptualizing and supporting global health research as a practice with increasingly discernable core characteristics.Through an exploration of these characteristics, the collection highlights practical, relevant and transferable lessons for consideration by researchers, their research-user partners, and donors working to address health inequities through global health research partnerships.The value of global health research partnerships is illustrated through the achievements of the collaborations featured in this collection.The ten case studies included in this collection do not describe individual research projects.Instead, they each provide an in-depth account of a defined program of research that acts as a platform for theoretically linked research projects.The programs are an integrated blend of knowledge generation, capacity building, and knowledge translation activities that have evolved towards increasing complexity and sophistication.In particular, attention to capacity building and knowledge translation increases as the programs mature over time.The programs of research are animated by a core alliance of individuals whose international partnerships are rooted in mutual trust and the articulation of a common goal: health equity.The cases presented in this collection are concerned with health inequities experienced by certain population groups.For example, the two cases set in South Asia (Haddad et al., Mumtaz et al.) are both concerned with the persistent health inequities that are experienced by lower-caste women belonging to marginalized indigenous groups.Another disadvantaged population group highlighted twice in this collection is people living with HIV/ AIDS in rural Sub-Saharan Africa (Kipp et al., Sodhi et al.).A third group, Ecuadorians with limited resources who are vulnerable to environmental degradation and to acute pesticide poisoning, is also highlighted twice in this
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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 | 0.048 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".