Public Health Physician Specialty Training in Canada and the United States
Bibliographic record
Abstract
Today's interconnected world has produced a distinct need for physician specialists in public health and preventive medicine. As the industrialized world confronts aging populations, rising health care costs, and a growing epidemic of chronic disease, it is clear that the focus of health care must become more preventive than curative.Although public health and preventive medicine exists in various forms worldwide, the literature has not yet examined different national strategies for postgraduate medical training in this unique specialty. This examination of present-day public health physician training in Canada and the United States represents a first step in addressing this gap.Using a standardized template for review, the authors compare key aspects of public health physician specialty training in both countries, including the definition and scope of the specialty; oversight and location of training; length of postgraduate training; specific clinical, academic, and practicum requirements; residency program funding; availability of residency positions; certification; and the roles of specialists.The authors explore similarities and differences between public health physician specialists in Canada and the United States in an effort to highlight training improvements for incorporation into each country's training program and to identify potential avenues of collaboration and cooperation across the border.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.025 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".