Developing evidence briefs for policy: a qualitative case study comparing the process of using a guidance-contextualization workbook in Peru and Uganda
Bibliographic record
Abstract
BACKGROUND: Translating research evidence from global guidance into policy can help strengthen health systems. A workbook was developed to support the contextualization of the WHO's 'Optimizing health worker roles to improve maternal and newborn health' (OptimizeMNH) guidance. This study evaluated the use of the workbook for the development of evidence briefs in two countries - Peru and Uganda. Findings surrounding contextual factors, steps in the process and evaluation of the workbook are presented. METHODS: A qualitative embedded case study was used. The case was the process of using the workbook to support the contextualization of global health systems guidance, with local evidence, to develop evidence briefs. Criterion sampling was used to select the countries, participants for interviews and documents included in the study. A template-organizing style and constant comparison were used for data analysis. RESULTS: A total of 19 participant-observation sessions and 8 interviews were conducted, and 50 documents were reviewed. Contextual factors, including the cadres, or groups, of health workers available in each country, the way the problem and its causes were framed, potential policy options to address the problem, and implementation considerations for these policy options, varied substantially between Peru and Uganda. However, many similarities were found in the process of using the workbook. Overall, the workbook was viewed positively and participants in both countries would use it again for other topics. CONCLUSIONS: Organizations that produce global guidance, such as WHO, need to consider institutionalizing the application of the workbook into their guidance development processes to help users at the national/subnational level create actionable and context-relevant policies. Feedback mechanisms also need to be established so that the evidence briefs and health policies arising from global guidance are tracked and the findings coming out of such guideline contextualization processes can be taken into consideration during future guidance development and research priority-setting.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".