Improving KT tools and products: development and evaluation of a framework for creating optimized, Knowledge-activated Tools (KaT)
Bibliographic record
Abstract
Abstract Background Positive impacts of quality improvement initiatives on health care and services have not been substantial. Knowledge translation (KT) strategies (tools, products and interventions) strive to facilitate the uptake of knowledge thereby the potential to improve care, but there is little guidance on how to develop them. Existing KT guidance or planning tools fall short in operationalizing all aspects of KT practice activities conducted by knowledge users (researchers, clinicians, patients, decision-makers), and most do not consider their variable needs or to deliver recommendations that are most relevant and useful for them. Methods We conducted a 3-phase study. In phase 1, we used several sources to develop a conceptual framework for creating optimized Knowledge-activated Tools (KaT) (consultation with our integrated KT team, the use of existing KT models and frameworks, findings of a systematic review of multimorbidity interventions and a literature review and document analysis on existing KT guidance tools). In phase 2, we invited KT experts to participate in a Delphi study to refine and evaluate the conceptual KaT framework. In phase 3, we administered an online survey to knowledge users (researchers, clinicians, decision-makers, trainees) to evaluate the potential usefulness of an online mock-up version of the KaT framework. Results We developed the conceptual KaT framework, and iteratively refined it with 35 KT experts in a 3-round Delphi study. The final framework represents the blueprint for what is needed to create KT strategies. Feedback from 201 researcher, clinician, decision-maker and trainee knowledge users on the potential need and usefulness of an online, interactive version of KaT indicated that they liked the idea of it (mean score 4.36 on a 5-point Likert scale) and its proposed features (mean score range 4.30–4.79). Conclusions Our findings suggest that mostly Canadian KT experts and knowledge users perceived the KaT framework and the future development of an online, interactive version to be important and needed. We anticipate that the KaT framework will provide clarity for knowledge users about how to identify their KT needs and what activities can address these needs, and to help streamline the process of these activities to facilitate efficient uptake of knowledge.
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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 | 0.162 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".