More Than "Using Research": The Real Challenges in Promoting Evidence-Informed Decision-Making
Bibliographic record
Abstract
OBJECTIVES AND METHODS: Seventeen focus groups and 53 semi-structured individual interviews involving 205 planners and decision-makers were conducted in all 11 Regional Health Authorities (RHAs) in the province of Manitoba, Canada. Objectives were to explore perspectives on the nature and use of "evidence," and barriers to evidence-informed decision-making (EIDM). RESULTS: In spite of almost universal support in principle for using evidence in decision-making, there was little consensus among participants on what evidence is, what kind of evidence is most appropriate and how "using evidence" can best be demonstrated. Significant skepticism about EIDM was expressed. Issues related to workload, politicized decision-making and organizational factors dominated the discussion of decision-makers. Barriers to EIDM were commonly attributed to factors external to the RHAs. CONCLUSION: Effective strategies to promote EIDM must address the multiple barriers experienced by decision-makers in a complex decision-making environment. Rather than simply focusing on issues of access to evidence or development of individual capacity, strategies must focus on changing decision-making processes to support appropriate use of evidence.
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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 | 0.512 | 0.454 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.020 | 0.099 |
| Scholarly communication | 0.050 | 0.041 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".