The Power of Rapid Reviews for Bridging the Knowledge-to-Action Gap in Evidence-Based Virtual Health Care
Bibliographic record
Abstract
Despite the surge in popularity of virtual health care services as a means of delivering health care through technology, the integration of research evidence into practice remains a challenge. Rapid reviews, a type of time-efficient evidence synthesis, offer a potential solution to bridge the gap between knowledge and action. This paper aims to highlight the experiences of the Fraser Health Authority's Virtual Health team in conducting rapid reviews. This paper discusses the experiences of the Virtual Health team in conducting 15 rapid reviews over the course of 1.5 years and the benefit of involving diverse stakeholders including researchers, project and clinical leads, and students for the creation of user-friendly knowledge products to summarize results. The Virtual Health team found rapid reviews to be a valuable tool for evidence-informed decision-making in virtual health care. Involving stakeholders and focusing on implementation considerations are crucial for maximizing the impact of rapid reviews. Health care decision makers are encouraged to consider implementing rapid review processes to improve the translation of research evidence into practice, ultimately enhancing patient outcomes and promoting a culture of evidence-informed care.
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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 | 0.630 | 0.807 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.009 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.040 | 0.044 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".