Five Domains of a Conceptual Framework of Continuing Professional Development
Bibliographic record
Abstract
ABSTRACT: Continuing professional development (CPD) for health professionals involves efforts at improving health of individuals and the population through educational activities of health professionals who previously attained a recognized level of acceptable proficiency (licensure). However, those educational activities have inconsistently improved health care outcomes of patients. We suggest a conceptual change of emphasis in designing CPD to better align it with the goals of improving health care value for patients through the dynamic incorporation of five distinct domains to be included in learning activities. We identify these domains as: (1) identifying, appraising, and learning new information [New Knowledge]; (2) ongoing practicing of newly or previously acquired skills to maintain expertise [New Skills and Maintenance]; (3) sharing and transfer of new learning for the health care team which changes their practice [Teams]; (4) analyzing data to identify problems and drive change resulting in improvements in the health care system and patient outcomes [Quality Improvement]; and (5) promoting population health and prevention of disease [Prevention]. We describe how these five domains can be integrated into a comprehensive conceptual framework of CPD, supported by appropriate learning theories that align with the goals of the health care delivery system. Drawing on these distinct but interrelated areas of CPD will help organizers and directors of learning events to develop their activities to meet the goals of learners and the health care system.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".