Priority setting for new systematic reviews: processes and lessons learned in three regions in Africa
Bibliographic record
Abstract
Priority setting to identify topical and context relevant questions for systematic reviews involves an explicit, iterative and inclusive process. In resource-constrained settings of low-income and middle-income countries, priority setting for health related research activities ensures efficient use of resources. In this paper, we critically reflect on the approaches and specific processes adopted across three regions of Africa, present some of the outcomes and share the lessons learnt while carrying out these activities. Priority setting for new systematic reviews was conducted between 2016 and 2018 across three regions in Africa. Different approaches were used: Multimodal approach (Central Africa), Modified Delphi approach (West Africa) and Multilevel stakeholder discussion (Southern-Eastern Africa). Several questions that can feed into systematic reviews have emerged from these activities. We have learnt that collaborative subregional efforts using an integrative approach can effectively lead to the identification of region specific priorities. Systematic review workshops including discussion about the role and value of reviews to inform policy and research agendas were a useful part of the engagements. This may also enable relevant stakeholders to contribute towards the priority setting process in meaningful ways. However, certain shared challenges were identified, including that emerging priorities may be overlooked due to differences in burden of disease data and differences in language can hinder effective participation by stakeholders. We found that face-to-face contact is crucial for success and follow-up engagement with stakeholders is critical in driving acceptance of the findings and planning future progress.
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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 | 0.469 | 0.470 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".