Competency-based continuing professional development
Bibliographic record
Abstract
Competence is traditionally viewed as the attainment of a static set of attributes rather than a dynamic process in which physicians continuously use their practice experiences to "progress in competence" toward the attainment of expertise. A competency-based continuing professional development (CPD) model is premised on a set of learning competencies that include the ability to (a) use practice information to identify learning priorities and to develop and monitor CPD plans; (b) access information sources for innovations in development and new evidence that may potentially be integrated into practice; (c) establish a personal knowledge management system to store and retrieve evidence and to select and manage learning projects; (d) construct questions, search for evidence, and record and track conclusions for practice; and (e) use tools and processes to measure competence and performance and develop action plans to enhance practice. Competency-based CPD emphasizes self-directed learning processes and promotes the role of assessment as a professional expectation and obligation. Various approaches to defining general competencies for practice require the creation of specific performance metrics to be meaningful and relevant to the lifelong learning strategies of physicians. This paper describes the assumptions, advantages, and challenges of establishing a CPD system focused on competencies that improve physician performance and the quality and safety of patient care. Implications for competency-based CPD are discussed from an individual and organizational perspective, and a model to bridge the transition from residency to practice is explored.
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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".