Health Research Profile to assess the capacity of low and middle income countries for equity-oriented research
Bibliographic record
Abstract
BACKGROUND: The Commission on Health Research for Development concluded that "for the most vulnerable people, the benefits of research offer a potential for change that has gone largely untapped." This project was designed to assess low and middle income country capacity and commitment for equity-oriented research. METHODS: A multi-disciplinary team with coordinators from each of four regions (Asia, Latin America, Africa and Central and Eastern Europe) developed a questionnaire through consensus meetings using a mini-Delphi technique. Indicators were selected based on their quality, validity, comprehensiveness, feasibility and relevance to equity. Indicators represented five categories that form the Health Research Profile (HRP): 1) Research priorities; 2) Resources (amount spent on research); 3) Production of knowledge (capacity); 4) Packaging of knowledge and 5) Evidence of research impact on policy and equity. We surveyed three countries from each region. RESULTS: Most countries reported explicit national health research priorities. Of these, half included specific research priorities to address inequities in health. Data on financing were lacking for most countries due to inadequate centralized collection of this information. The five main components of HRP showed a gradient where countries scoring lower on the Human Development Index (HDI) had a lower capacity to conduct research to meet local health research needs. Packaging such as peer-reviewed journals and policy forums were reported by two thirds of the countries. Seven out of 12 countries demonstrated impact of health research on policies and reported engagement of stakeholders in this process. CONCLUSION: Only one out of 12 countries indicated there was research on all fronts of the equity debate. Knowledge sharing and management is needed to strengthen within-country capacity for research and implementation to reduce inequities in health. We recommend that all countries (and external agencies) should invest more in building a certain minimum level of national capacity for equity-oriented research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".