Evidence implementation: Development of an online methodology from the knowledge‐to‐action model of knowledge translation
Bibliographic record
Abstract
This paper describes an online facilitation for operationalizing the knowledge-to-action (KTA) model. The KTA model incorporates implementation planning that is optimally suited to the information needs of clinicians. The can-implement(©) is an evidence implementation process informed by the KTA model. An online counterpart, the can-implement.pro(©) , was developed to enable greater dissemination and utilization of the can-implement(©) process. The driver for this work was health professionals' need for facilitation that is iterative, informed by context and localized to the specific needs of users. The literature supporting this paper includes evaluation studies and theoretical concepts relevant to KTA model, evidence implementation and facilitation. Nursing and other health disciplines require a skill set and resources to successfully navigate the complexity of organizational requirements, inter-professional leadership and day-to-day practical management to implement evidence into clinical practice. The can-implement.pro(©) provides an accessible, inclusive system for evidence implementation projects. There is empirical support for evidence implementation informed by the KTA model, which in this phase of work has been developed for online uptake. Nurses and other clinicians seeking to implement evidence could benefit from the directed actions, planning advice and information embedded in the phases and steps of can-implement.pro(©) .
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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 | 0.099 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".