The development of national entrustable professional activities to inform the training and assessment of public health and preventative medicine residents
Bibliographic record
Abstract
BACKGROUND: Entrustable Professional Activities (EPAs) have emerged to bridge the gap between the learning of individual competencies and competence in real world practice. EPAs capture the critical core work of a discipline integrating competencies from multiple domains. This report describes the development of a set of EPAs for specialty training in Public Health and Preventive Medicine (PHPM) in Canada. METHODS: The PHPM EPAs were developed using multiple existing sources. A combination of workshops and a national online survey was used to consult with PHPM program directors, the national specialty committee, and competency-based education experts. RESULTS: A national survey of PHPM program directors had a 71% response rate with 80% or more of respondents agreeing with all of the 20 EPA titles and all but one of their descriptions. Competency developmental stage-specific milestones were identified for each EPA. CONCLUSION: The identification of the EPAs and their milestones will increase emphasis on the demonstrated performance of the specialty's core work. Simulations applicable to several EPAs have been developed. The EPAs have also been incorporated into a PHPM National Review Course and will be used to develop a national PHPM curriculum, as well as a national written practice examination.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".