Building capacity for implementation—the KT Challenge
Bibliographic record
Abstract
BACKGROUND: The KT Challenge program supports health care professionals to effectively implement evidence-based practices. Unlike other knowledge translation (KT) programs, this program is grounded in capacity building, focuses on health care professionals (HCPs), and uses a multi-component intervention. This study presents the evaluation of the KT Challenge program to assess the impact on uptake, KT capacity, and practice change. METHODS: The evaluation used a mixed-methods retrospective pre-post design involving surveys and review of documents such as teams' final reports. Online surveys collecting both quantitative and qualitative data were deployed at four time points (after both workshops, 6 months into implementation, and at the end of the 2-year funded projects) to measure KT capacity (knowledge, skills, and confidence) and impact on practice change. Qualitative data was analyzed using a general inductive approach and quantitative data was analyzed using non-parametric statistics. RESULTS: Participants reported statistically significant increases in knowledge and confidence across both workshops, at the 6-month mark of their projects, and at the end of their projects. In addition, at the 6-month check-in, practitioners reported statistically significant improvements in their ability to implement practice changes. In the first cohort of the program, of the teams who were able to complete their projects, half were able to show demonstrable practice changes. CONCLUSIONS: The KT Challenge was successful in improving the capacity of HCPs to implement evidence-based practice changes and has begun to show demonstrable improvements in a number of practice areas. The program is relevant to a variety of HCPs working in diverse practice settings and is relatively inexpensive to implement. Like all practice improvement programs in health care settings, a number of challenges emerged stemming from the high turnover of staff and the limited capacity of some practitioners to take on anything beyond direct patient care. Efforts to address these challenges have been added to subsequent cohorts of the program and ongoing evaluation will examine if they are successful. The KT Challenge program has continued to garner great interest among practitioners, even in the midst of dealing with the COVID-19 pandemic, and shows promise for organizations looking for better ways to mobilize knowledge to improve patient care and empower staff. This study contributes to the implementation science literature by providing a description and evaluation of a new model for embedding KT practice skills in health care settings.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".