Twelve tips for bringing competencies into continuing professional development: Curriculum mapping
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. There is a growing worldwide awareness in the field of health professions education and research that a successful implementation of competency-based medical education (CBME) requires embracing all stages of professional development (from undergraduate, through residency to continuing education). However, despite increased levels of cognizance and even enthusiasm about the importance of the entire continuum for the ultimate goal of improved healthcare, much work still remains as CBME principles are not widely adopted in continuing professional development (CPD). Much has been written about the process of competency-based curriculum development (e.g., the formation and development of meaningful and measurable outcomes) in undergraduate studies and postgraduate training, but not in CPD. If we expect a CPD curriculum to integrate CBME, competencies must be developed and clearly specified how they will fit into a coherent and implementable curriculum structure. In this article, we describe existing practices some educational institutions have, including our experiences in the Office of CPD at the University of Ottawa, Canada, in designing a competency-based curriculum and provide 12 tips for those who begin their journey of organizing, developing, and implementing such curricula. We conclude that in order to translate a competency-based approach into CPD, educational programs will have to refine curricula across health professionals' education using curriculum mapping as an important tool of curriculum development and evaluation.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".