The roles and influence of actors in the uptake of evidence: the case of malaria treatment policy change in Uganda
Bibliographic record
Abstract
BACKGROUND: Uganda changed its malaria treatment policy in response to evidence of resistance to commonly used antimalarials. The use of evidence in policy development--also referred to as knowledge translation (KT)--is crucial, especially in resource-limited settings. However, KT processes occur amidst a complex web of stakeholder interactions. Stakeholder involvement in evidence generation and in KT activities is essential. In the present study, we explored how stakeholders impacted the uptake of evidence in the malaria treatment policy change in Uganda. METHODS: We employed a qualitative case study methodology involving interviews with key informants and review of documents. A timeline of events was developed, which guided the purposive sampling of respondents and identification of relevant documents. Data were analysed using inductive content analysis techniques. RESULTS: Stakeholders played multiple roles in evidence uptake in the malaria treatment policy change. Donors, the Ministry of Health (MoH), service providers, and researchers engaged in the role of evidence generation. The MoH, parliamentarians, and opinion leaders at the national and local levels engaged in dissemination of evidence. The donors, MoH, researchers, and service providers engaged in the uptake of evidence in policy development and implementation. Stakeholders exerted varying levels of support and influence for different reasons. It is noteworthy that all of the influential stakeholders were divided regarding the best antimalarial alternative to adopt. CONCLUSION: Our results showed a diverse group of stakeholders who played multiple roles, with varying levels of support and influence on the uptake of evidence in the malaria treatment policy change. For a given KT processes, mapping the relevant stakeholders and devising mechanism for their engagement and for how to resolve conflicts of interest and disagreements a priori will enhance uptake of evidence in policy development.
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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 | 0.113 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.031 | 0.030 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".