Making health systems research work: time to shift funding to locally-led research in the South
Bibliographic record
Abstract
This week, the global health systems research community is gathered in Vancouver, Canada, for the Fourth Global Symposium on Health Systems Research. The current movement for health systems research developed out of a need to strengthen health systems in low-income and middle-income countries. More than 25 years ago, the Commission on Health Research for Development published a report that represented a pivotal change in thinking about health research for development.1 The main argument of the report was that research contributed little to health in low-income and middle-income countries, because it matched poorly with needs in the global South, was dominated by researchers from the North, and had a narrow biomedical focus.
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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 | 0.147 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.040 | 0.044 |
| Open science | 0.007 | 0.047 |
| Research integrity | 0.033 | 0.053 |
| Insufficient payload (model declined to judge) | 0.062 | 0.020 |
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".