Evaluating health research priority-setting in low-income countries: a case study of health research priority-setting in Zambia
Bibliographic record
Abstract
Priority-setting (PS) for health research presents an opportunity for the relevant stakeholders to identify and create a list of priorities that reflects the country's knowledge needs. Zambia has conducted several health research prioritisation exercises that have never been evaluated. Evaluation would facilitate gleaning of lessons of good practices that can be shared as well as the identification of areas of improvement. This paper describes and evaluates health research PS in Zambia from the perspectives of key stakeholders using an internationally validated evaluation framework. METHODS: This was a qualitative study based on 28 in-depth interviews with stakeholders who had participated in the PS exercises. An interview guide was employed. Data were analysed using NVIVO 10. Emerging themes were, in turn, compared to the framework parameters. RESULTS: Respondents reported that, while the Zambian political, economic, social and cultural context was conducive, there was a lack of co-ordination of funding sources, partners and research priorities. Although participatory, the process lacked community involvement, dissemination strategies and appeals mechanisms. Limited funding hampered implementation, monitoring and evaluation. Research was largely driven by the research funders. CONCLUSIONS: Although there is apparent commitment to health research in Zambia, health research PS is limited by lack of funding, and consistently used explicit and fair processes. The designated national research organisation and the availability of tools that have been validated and pilot tested within Zambia provide an opportunity for focused capacity strengthening for systematic prioritisation, monitoring and evaluation. The utility of the evaluation framework in Zambia could indicate potential usefulness in similar low-income countries.
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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 | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Case report | low |
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 | 0.376 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.006 |
| 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, 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".