Do knowledge translation (KT) plans help to structure KT practices?
Bibliographic record
Abstract
BACKGROUND: A knowledge translation (KT) planning template is a roadmap laying out the core elements to be considered when structuring the implementation of KT activities by researchers and practitioners. Since 2010, the Institut national de santé publique du Québec (INSPQ; Québec Public Health Institute) has provided tools and guidance to in-house project teams to help them develop KT plans. This study sought to identify the dimensions included in those plans and which ones were integrated and how. The results will be of interest to funding agencies and scientific organizations that provide frameworks for KT planning. METHODS: The operationalization of KT planning dimensions was assessed in a mixed methods case study of 14 projects developed at the INSPQ between 2010 and 2013. All plans were assessed (rated) using an analytical tool developed for this study and data from interviews with the planning coordinators. The analytical tool and interview guide were based on eight core KT dimensions identified in the literature. RESULTS: Analysis of the plans and interviews revealed that the dimensions best integrated into the KT plans were 'analysis of the context (barriers and facilitators) and of users' needs', 'knowledge to be translated', 'KT partners', 'KT strategies' and, to a lesser extent, 'overall KT approach'. The least well integrated dimensions were 'knowledge about knowledge users', 'KT process evaluation' and 'resources'. CONCLUSIONS: While the planning coordinators asserted that a plan did not need to include all the dimensions to ensure its quality and success, nevertheless the dimensions that received less attention might have been better incorporated if they had been supported with more instruments related to those dimensions and sustained methodological guidance. Overall, KT planning templates appear to be an appreciated mechanism for supporting KT reflexive practices. Based on this study and our experience, we recommend using KT plans cautiously when assessing project efficacy and funding.
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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 | 0.092 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".