Continuing education at the cutting edge: Promoting transformative knowledge translation
Bibliographic record
Abstract
As the evidence-based practice movement gains momentum, continuing education practitioners increasingly confront the challenge of developing and conducting opportunities for achieving research uptake. Recent thinking invites new approaches to continuing education for health professionals, with due consideration of what knowledge merits uptake by practitioners, who should play what role in the knowledge transfer process, and what educational approach should be used. This article presents an innovative theory-based strategy that encompasses this new perspective. Through a facilitated experience of perspective transformation, clinicians are engaged in an on-the-job process of developing a deeply felt interest in research findings relevant to everyday practice, as well as ownership of that knowledge and its application. The strategy becomes a sustainable, integrated part of clinical practice, fitting naturally within its dynamic, unique environment, context, and climate and overcoming the barrier of time. Clinician experience of a top-down push toward prescribed practice change is avoided. With an expanded role encompassing facilitation of active learning partnerships for practice change, the continuing educator fosters a learning organization culture across the institution. The resultant role changes and leadership and accountability issues are elaborated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.061 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".