Translating evidence into practice: the role of health research funders
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.
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.
How this classification was reachedexpand
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 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 | 0.593 | 0.658 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.019 | 0.050 |
| Scholarly communication | 0.071 | 0.043 |
| Open science | 0.010 | 0.059 |
| Research integrity | 0.026 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".