Translating evidence into practice: the role of health research funders
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
BACKGROUND: A growing body of work on knowledge translation (KT) reveals significant gaps between what is known to improve health, and what is done to improve health. The literature and practice also suggest that KT has the potential to narrow those gaps, leading to more evidence-informed healthcare. In response, Canadian health research funders and agencies have made KT a priority. This article describes how one funding agency determined its KT role and in the process developed a model that other agencies could use when considering KT programs. DISCUSSION: While 'excellence' is an important criterion by which to evaluate and fund health research, it alone does not ensure relevance to societal health priorities. There is increased demand for return on investments in health research in the form of societal and health system benefits. Canadian health research funding agencies are responding to these demands by emphasizing relevance as a funding criterion and supporting researchers and research users to use the evidence generated.Based on recommendations from the literature, an environmental scan, broad circulation of an iterative discussion paper, and an expert working group process, our agency developed a plan to maximize our role in KT. Key to the process was development of a model comprising five key functional areas that together create the conditions for effective KT: advancing KT science; building KT capacity; managing KT projects; funding KT activities; and advocating for KT. Observations made during the planning process of relevance to the KT enterprise are: the importance of delineating KT and communications, and information and knowledge; determining responsibility for KT; supporting implementation and evaluation; and promoting the message that both research and KT take time to realize results. SUMMARY: Challenges exist in fulfilling expectations that research evidence results in beneficial impacts for society. However, health agencies are well placed to help maximize the use of evidence in health practice and policy. We propose five key functional areas of KT for health agencies, and encourage partnerships and discussion to advance the field.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.104 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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