The quest for a framework for sustainable and institutionalised priority-setting for health research in a low-resource setting: the case of Zambia
Bibliographic record
Abstract
BACKGROUND: Priority-setting for health research in low-income countries remains a major challenge. While there have been efforts to systematise and improve the processes, most of the initiatives have ended up being a one-off exercise and are yet to be institutionalised. This could, in part, be attributed to the limited capacity for the priority-setting institutions to identify and fund their own research priorities, since most of the priority-setting initiatives are driven by experts. This paper reports findings from a pilot project whose aim was to develop a systematic process to identify components of a locally desirable and feasible health research priority-setting approach and to contribute to capacity strengthening for the Zambia National Health Research Authority. METHODS: Synthesis of the current literature on the approaches to health research prioritisations. The results of the synthesis were presented and discussed with a sample of Zambian researchers and decision-makers who are involved in health research priority-setting. The ultimate aim was for them to explore the different approaches available for guiding health research priority-setting and to identify an approach that would be relevant and feasible to implement and sustain within the Zambian context. RESULTS: Based on the evidence that was presented, the participants were unable to identify one approach that met the criteria. They identified attributes from the different approaches that they thought would be most appropriate and proposed a process that they deemed feasible within the Zambian context. CONCLUSION: While it is easier to implement prioritisation based on one approach that the initiator might be interested in, researchers interested in capacity-building for health research priority-setting organisations should expose the low-income country participants to all approaches. Researchers ought to be aware that sometimes one shoe may not fit all, as in the case of Zambia, instead of choosing one approach, the stakeholders may select desirable attributes from the different approaches and piece together an approach that would be feasible and acceptable within their context. An approach that builds on the decision-makers' understanding of their contexts and their input to its development would foster local ownership and has a greater potential for sustainability.
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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: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.238 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.024 | 0.061 |
| Scholarly communication | 0.033 | 0.023 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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.
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".