Roadmap for a Participatory Research–Practice Partnership to Implement Evidence
Bibliographic record
Abstract
BACKGROUND: Our research team has undertaken implementation of evidence in the form of practice guideline recommendations for populations in hospital, community, and long-term care settings with diverse provider and patient populations (people with chronic wounds, e.g., pressure and leg ulcers, heart failure, stroke, diabetes, palliative care, cancer, and maternity care). Translating evidence into clinical practice at the point of care is a complex and often overwhelming challenge for the health system as well as for individual practitioners. PURPOSE: To ensure that best available evidence is integrated into practice, "local evidence" needs to be generated and this process accomplishes a number of things: it focuses all involved on the "same page," identifies important facilitating factors as well as barriers, provides empirical support for planning, and in itself is a key aspect of implementation. In doing this work, we developed a roadmap, the Queen's University Research Roadmap for Knowledge Implementation (QuRKI) that outlines three major phases of linked research and implementation activity: (1) issue identification/clarification; (2) solution building; and (3) implementation, evaluation, and nurturing the change. In this paper, we describe our practical experience as researchers working at point-of-care and how research can be used to facilitate the implementation of evidence. An exemplar is used to illustrate the fluid interplay of research and implementation activities and present the range of supporting research. IMPLICATIONS: QuRKI serves as a guide for researchers in the formation of a strategic alliance with the practice community for undertaking evidence-informed reorganization of care. Using this collaborative approach, researchers play an integral role in focusing on, and using evidence during all discussions. We welcome further evaluation of its usefulness in the field.
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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 | 0.387 | 0.242 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.019 | 0.025 |
| Scholarly communication | 0.033 | 0.037 |
| Open science | 0.013 | 0.057 |
| Research integrity | 0.031 | 0.037 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".