Supporting the Use of Research Evidence in the Canadian Health Sector
Bibliographic record
Abstract
Interventions to support evidence-informed decision making have increased in recent years, but they are often fragmented across different clinical, management and policy environments.Many of these efforts also place varying emphasis on supporting the use of research evidence, with some choosing to focus more on expert knowledge and/or media coverage and others focusing on supporting the use of actionable messages arising from high-quality, relevant and optimally packaged research evidence.In this paper, we profile five Canadian contributions -EvidenceUpdates, Rx for Change, Health-Evidence.ca,Health Systems Evidence and the McMaster Health Forum -that allow providers, managers and policy makers to efficiently find and use research evidence when they need it.These contributions are critical for supporting both local and global efforts to provide optimal and costeffective care, improving the quality of care and strengthening health systems.esearch evidence is an important input into decision making for both healthcare providers and for health system managers and policy makers.Research evidence can inform decisions about which programs, services and drugs to provide as well as decisions both about health systems (i.e., strengthening or reforming health system governance, financial and delivery arrangements within which programs, services and drugs are provided) and within health systems (i.e., how to get cost-effective programs, services
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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 | 0.416 | 0.608 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.031 | 0.024 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.037 | 0.011 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".