STRATEGIES TO FACILITATE EVIDENCE-INFORMED AND PARTICIPATORY HEALTH POLICY MAKING IN ETHIOPIA
Bibliographic record
Abstract
Evidence-informed health policy making contributes to improved health outcomes by strengthening health systems. In addition, health policy decisions should take into consideration the needs and priorities of users of healthcare services. However, little research has been done to find best ways to facilitate evidence-informed and participatory health policymaking, particularly in low- and middle-income countries. This thesis is written based on three studies done in Ethiopia to fill this knowledge gap. In the first study, we examined whether, how and under what conditions evidence was used and service-users participated during the agenda-setting and policy formulation phases of selected policies in the ‘prevention of mother-to-child transmission of HIV’ program in Ethiopia using a multiple-case study design. In the second study, we identified strategies to facilitate evidence-informed health policy making using an online survey. In the third study, we identified strategies to facilitate participatory health policy making using a combined paper-based and Internet-based Delphi approach. The thesis does not have direct theoretical contribution. However, it will draw on two theoretical frameworks, namely Kingdon’s framework and the 3I+E framework. and use them in a setting from where they were originally developed. This thesis has two substantive and three methodological contributions. Substantively, the first study provides empirical evidence about the current practice of evidence-informed and participatory health policy making in a low-income, ‘revolutionary’ democratic country (Ethiopia). In addition, the studies have identified strategies to concretize the constitutional and policy provisions for evidence-informed and participatory health policy making in Ethiopia. The thesis has the following three methodological contributions. First, the studies explored the use of Kingdon’s multiple-streams framework and the 3I+E framework in predicting factors influencing agenda-setting and policy formulation phases, respectively, and in explaining the use of research evidence in informing these two phases in a ‘revolutionary’ democratic country where they have not previously been used. Second, the thesis has shown that paper-based and Internet-based Delphi could be combined in contexts with limited resources. Third, the thesis has demonstrated the possibility of training service-users as ‘peer’ researchers to collect and analyze data to inform their participation and maximize their contribution in surveys, forming a pyramid of participation.
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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 | 0.108 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| 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, 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".