Knowledge translation: an overview and recommendations in relation to the Fourth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia
Bibliographic record
Abstract
The growing population of persons with dementia in Canada and the provision of quality care for this population is an issue that no healthcare authority will escape. Physicians often view dementia as a difficult and time-consuming condition to diagnose and manage. Current evidence must be effectively transformed into usable recommendations for physicians; however, we know that use of evidence-based practice recommendations is a challenge in all realms of medical care, and failure to utilize these leads to less than optimal care for patients. Despite this expanding need for readily available resources, knowledge translation (KT) is often seen as a daunting, if not confusing, undertaking for researchers. Here we offer a brief introduction to the processes around KT, including terms and definitions, and outline some common KT frameworks including the knowledge to action cycle, the Promoting Action on Research Implementation in Health Services framework and the Consolidated Framework for Implementation Research. We also outline practical steps for planning and executing a KT strategy particularly around the implementation of recommendations for practice, and offer recommendations for KT planning in relation to the Fourth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia.
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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 | 0.286 | 0.342 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.041 | 0.047 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.015 | 0.014 |
| Research integrity | 0.025 | 0.028 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier 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".