Mapping the Transition to Competence By Design in Public Health and Preventive Medicine in Canada
Bibliographic record
Abstract
Public Health and Preventive Medicine (PHPM) is a 5-year Royal College of Physicians and Surgeons of Canada (RCPSC) postgraduate medical training program which aims to prepare specialists who work to safeguard and improve the health of populations. In Canada, PHPM offers a broad scope of practice that includes different fields such as clinical, academic, administrative, and/or a mix of any of those. Most commonly, PHPM training prepares graduates to practice as a Medical Officer of Health (MOH) or equivalent. This role serves to protect and promote the wellbeing of populations based on principles of biostatistics and epidemiology, environmental health, social and behavioral sciences, health program planning and policy, management, health economics, and relevant biological and social sciences. PHPM training varies across provinces depending on public health systems and structures. Consequently, implementation of Competence By Design (CBD) in this specialty is inherently challenging. CBD is the outcome-based medical education model mandated by the RCPSC to be adopted in all postgraduate medical training programs in Canada. This constructivist grounded theory (CGT) based study involved 35 iteratively conducted, semi-structured interviews which were analyzed through paired and parallel coding to explore and describe PHPM stakeholders’ perspectives about the landscape in PHPM training and practice in Canada as well as the anticipated changes required for a successful transition to CBD. Challenges of CBD implementation identified in this study included the non-standardized training path across Canada, the scarcity of financial and human resources to support this transition, and the lack of integration of the three different training components in this specialty (clinical, academic, and public health).
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".