More Than "Using Research": The Real Challenges in Promoting Evidence-Informed Decision-Making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it