Enhancing evidence informed policymaking in complex health systems: lessons from multi-site collaborative approaches
Bibliographic record
Abstract
BACKGROUND: There is an increasing interest worldwide to ensure evidence-informed health policymaking as a means to improve health systems performance. There is a need to engage policymakers in collaborative approaches to generate and use knowledge in real world settings. To address this gap, we implemented two interventions based on iterative exchanges between researchers and policymakers/implementers. This article aims to reflect on the implementation and impact of these multi-site evidence-to-policy approaches implemented in low-resource settings. METHODS: The first approach was implemented in Mexico and Nicaragua and focused on implementation research facilitated by communities of practice (CoP) among maternal health stakeholders. We conducted a process evaluation of the CoPs and assessed the professionals' abilities to acquire, analyse, adapt and apply research. The second approach, called the Policy BUilding Demand for evidence in Decision making through Interaction and Enhancing Skills (Policy BUDDIES), was implemented in South Africa and Cameroon. The intervention put forth a 'buddying' process to enhance demand and use of systematic reviews by sub-national policymakers. The Policy BUDDIES initiative was assessed using a mixed-methods realist evaluation design. RESULTS: In Mexico, the implementation research supported by CoPs triggered monitoring by local health organizations of the quality of maternal healthcare programs. Health programme personnel involved in CoPs in Mexico and Nicaragua reported improved capacities to identify and use evidence in solving implementation problems. In South Africa, Policy BUDDIES informed a policy framework for medication adherence for chronic diseases, including both HIV and non-communicable diseases. Policymakers engaged in the buddying process reported an enhanced recognition of the value of research, and greater demand for policy-relevant knowledge. CONCLUSIONS: The collaborative evidence-to-policy approaches underline the importance of iterations and continuity in the engagement of researchers and policymakers/programme managers, in order to account for swift evolutions in health policy planning and implementation. In developing and supporting evidence-to-policy interventions, due consideration should be given to fit-for-purpose approaches, as different needs in policymaking cycles require adapted processes and knowledge. Greater consideration should be provided to approaches embedding the use of research in real-world policymaking, better suited to the complex adaptive nature of health systems.
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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 | Qualitative | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Qualitative | 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 | 0.224 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.010 | 0.038 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".