How Do Clinicians Learn About Knowledge Translation? An Investigation of Current Web-Based Learning Opportunities
Bibliographic record
Abstract
BACKGROUND: Clinicians are important stakeholders in the translation of well-designed research evidence into clinical practice for optimal patient care. However, the application of knowledge translation (KT) theories and processes may present conceptual and practical challenges for clinicians. Online learning platforms are an effective means of delivering KT education, providing an interactive, time-efficient, and affordable alternative to face-to-face education programs. OBJECTIVE: This study investigates the availability and accessibility of online KT learning opportunities for health professionals. It also provides an analysis of the types of resources and associated disciplines retrieved by a range of KT synonyms. METHODS: We searched a range of bibliographic databases and the Internet (Google advanced option) using 9 KT terms to identify online KT learning resources. To be eligible, resources had to be free, aimed at clinicians, educational in intent, and interactive in design. Each term was searched using two different search engines. The details of the first 100 websites captured per browser (ie, n=200 results per term) were entered into EndNote. Each site was subsequently visited to determine its status as a learning resource. Eligible websites were appraised for quality using the AACODS (Authority, Accuracy, Coverage, Objectivity, Date, Significance) tool. RESULTS: We identified 971 unique websites via our multiple search strategies. Of these, 43 were health-related and educational in intent. Once these sites were evaluated for interactivity, a single website matched our inclusion criteria (Dementia Knowledge Translation Learning Centre). CONCLUSIONS: KT is an important but complex system of processes. These processes overlap with knowledge, practice, and improvement processes that go by a range of different names. For clinicians to be informed and competent in KT, they require better access to free learning opportunities. These resources should be designed from the viewpoint of the clinician, presenting KT's multifaceted theories and processes in an engaging, interactive way. This learning should empower clinicians to contextualize and apply KT strategies within their own 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 | 0.052 | 0.284 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".