Defining features of the practice of global health research: an examination of 14 global health research teams
Bibliographic record
Abstract
OBJECTIVES: This paper strives to develop a pragmatic view of the scope of practice and core characteristics of global health research (GHR) by examining the activities of 14 Canadian-funded global health teams that were in the process of implementing research programs. METHODS: Information was collected by a reflective exploration of team proposals and progress reports, a content analysis of the outputs from an all-team meeting and review of the literature. RESULTS: Teams adopted equity-centered, problem-focused, systems-based approaches intended to find upstream determinants that could make people more resilient to social and ecological factors impacting their health. Long-term visions and time frames were needed to develop and solidify fully functional interdisciplinary, multinational, multicultural partnerships. The implementation of research into practice was a motivating factor for all teams, but to do this, they recognized the need for evidence-based advice on how to best do this. Traditional measures of biomedical research excellence were necessary but not sufficient to encompass views of excellence of team-based interdisciplinary research, which includes features like originality, coherence and cumulative contributions to fields of study, acceptance by peers and success in translating research into gains in health status. An innovative and nuanced approached to GHR ethics was needed to deal with some unique ethical issues because the needs for GHR were not adequately addressed by institutional biomedical research ethics boards. Core competencies for GHR researchers were a blend of those needed for health promotion, population health, international development, sustainable development, and systems science. DISCUSSION: Developing acceptable and meaningful ways to evaluate the short-term contributions for GHR and forecast its long-term impacts is a strategic priority needed to defend decisions being made in GHR development. Planning and investing to support the underlying GHR elements and competencies that allow for adaptive, innovative, and supportive research partnerships to achieve 'health for all' are more likely to have long-term impacts than building research strategies around specific diseases of interest.
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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 | 0.190 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".