Comparison of public health and preventive medicine physician specialty training in six countries: Identifying challenges and opportunities
Bibliographic record
Abstract
Rationale: Public health and preventive medicine (PHPM) has been recognized internationally as a physician specialty, but national parallels and differences exist between training contexts. This paper reviews PHPM training and employment in Canada, France, Italy, Japan, the United Kingdom, and the USA. Methods: Information gathered from relevant accreditation bodies and literature searches was used to create descriptive profiles of national training demographics and structure and a narrative outlining trends and challenges facing the specialty. Results: Notable similarities and differences exist between national contexts. Key themes were differences in training strategies and practice scope, specialty stakeholders, certification structure, and funding. Recognition challenges faced the specialty across all six countries. Other challenges included unclear competencies and training strategies and a need for PHPM specialists to highlight their role in combating population health threats. Additional differences existed between comparator countries on the structure of training, funding sources for training programs, availability of training posts, and linkages with other physician specialties. Conclusion: Highlighting these themes is a first step to fostering training collaborations between PHPM specialist physicians to augment transnational action on global public health challenges and also supports PHPM physician educators with innovative solutions from abroad that might address domestic specialty challenges.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".