Advancing evidence‐based practice through the Knowledge Translation Challenge: Nurses’ important roles in research, implementation science and practice change
Bibliographic record
Abstract
AIM: To describe a knowledge translation capacity-building initiative and illustrate the roles of nurses in practice change using an exemplar case study. DESIGN: The report uses observational methods and reflection. METHODS: The Knowledge Translation Challenge program involves a multi-component intervention across several sites. The advisory committee invited eligible teams to attend capacity-building workshops. Implementation plans were developed, and successful teams receive funding for a 2 year period. Evaluation involved collecting data on program uptake and impact on practice change. Data has been collected from five cohorts. The exemplar case study employed an action-research framework. RESULTS: Four nurse-led teams have demonstrated successful implementation of their practice change. The case study on implementing a clinical toolkit for clozapine management further illustrates a thoughtful planning process, and implementation journey and learnings by a team of nurses. CONCLUSION: The Knowledge Translation Challenge program empowers nurses to use implementation science practices to enhance the quality and effectiveness of healthcare services. Success of this initiative serves as a model for addressing the persistent gap between knowledge and practice in clinical settings and the value of activating nurses to help close this gap. IMPLICATIONS: As the most trusted and numerous profession, it is vital that nurses contribute to efforts to translate research evidence into clinical practice. The Knowledge Translation Challenge program supports nurses to lead practice change. IMPACT: The Knowledge Translation Challenge program successfully equips nurses and other health care providers with the knowledge, skills and resources to implement practice improvements which enhance the quality and effectiveness of healthcare services and nursing practice. PATIENT OR PUBLIC CONTRIBUTION: The Knowledge Translation Challenge advisory committee has three patient-public partners that support teams to develop a patient-oriented approach for their projects by providing feedback on the implementation plans. Each team was also supported to include patient-public partners on their project.
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How this classification was reachedexpand
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 | 0.040 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.012 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".