Supporting the use of research evidence in decision-making in crisis zones in low- and middle-income countries: a critical interpretive synthesis
Bibliographic record
Abstract
BACKGROUND: Decision-makers in crisis zones are faced with the challenge of having to make health-related decisions under limited time and resource constraints and in light of the many factors that can influence their decisions, of which research evidence is just one. To address a key gap in the research literature about how best to support the use of research evidence in such situations, we conducted a critical interpretive synthesis approach to develop a conceptual framework that outlines the strategies that leverage the facilitators and address the barriers to evidence use in crisis zones. METHODS: We systematically reviewed both empirical and non-empirical literature and used an interpretive analytic approach to synthesise the results and develop the conceptual framework. We used a 'compass' question to create a detailed search strategy and conducted electronic searches in CINAHL, EMBASE, MEDLINE, SSCI and Web of Science. A second reviewer was assigned to a representative sample of articles. We purposively sampled additional papers to fill in conceptual gaps. RESULTS: We identified 21 eligible papers to be analysed and purposively sampled an additional 6 to fill conceptual gaps. The synthesis resulted in a conceptual framework that focuses on evidence use in crisis zones examined through the lens of four systems - political, health, international humanitarian aid and health research. Within each of the four systems, the framework identifies the most actionable strategies that leverage the facilitators and address the barriers to evidence use. CONCLUSIONS: This study presents a new conceptual framework that outlines strategies that leverage the facilitators and address the barriers to evidence use in crisis zones within different systems. This study expands on the literature pertaining to evidence-informed decision-making.
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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 | 0.611 | 0.763 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.081 | 0.043 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.041 | 0.036 |
| Open science | 0.013 | 0.022 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".