Paving the Way Forward for Evidence-Based Continuing Professional Development
Bibliographic record
Abstract
ABSTRACT: Continuing professional development (CPD) fosters lifelong learning and enables health care providers to keep their knowledge and skills current with rapidly evolving health care practices. Instructional methods promoting critical thinking and decision making contribute to effective CPD interventions. The delivery methods influence the uptake of content and the resulting changes in knowledge, skills, attitudes, and behavior. Educational approaches are needed to ensure that CPD meets the changing needs of health care providers. This article examines the development approach and key recommendations embedded in a CE Educator's toolkit created to evolve CPD practice and foster a learning experience that promotes self-awareness, self-reflection, competency, and behavioral change. The Knowledge-to-Action framework was used in designing the toolkit. The toolkit highlighted three intervention formats: facilitation of small group learning, case-based learning, and reflective learning. Strategies and guidelines to promote active learning principles in CPD activities within different modalities and learning contexts were included. The goal of the toolkit is to assist CPD providers to design educational activities that optimally support health care providers' self-reflection and knowledge translation into their clinical environment and contribute to practice improvement, thus achieving the outcomes of the quintuple aim.
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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 | 0.116 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 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".