Assessing capacity for health policy and systems research in low and middle income countries*
Bibliographic record
Abstract
BACKGROUND: As demand grows for health policies based on evidence, questions exist as to the capacity of developing countries to produce the health policy and systems research (HPSR) required to meet this challenge. METHODS: A postal/web survey of 176 HPSR producer institutions in developing countries assessed institutional structure, capacity, critical mass, knowledge production processes and stakeholder engagement. Data were projected to an estimated population of 649 institutions. RESULTS: HPSR producers are mostly small public institutions/units with an average of 3 projects, 8 researchers and a project portfolio worth $155,226. Experience, attainment of critical mass and stakeholder engagement are low, with only 19% of researchers at PhD level, although researchers in key disciplines are well represented and better qualified. Research capacity and funding are similar across income regions, although inequalities are apparent. Only 7% of projects are funded at $100,000 or more, but they account for 54% of total funding. International sources and national governments account for 69% and 26% of direct project funding, respectively. A large proportion of international funds available for HPSR in support of developing countries are either not spent or spent through developed country institutions. CONCLUSIONS: HPSR producers need to increase their capacity and critical mass to engage effectively in policy development and to absorb a larger volume of resources. The relationship between funding and critical mass needs further research to identify the best funding support, incentives and capacity strengthening approaches. Support should be provided to network institutions, concentrate resources and to attract funding.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.107 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".