Doctors of osteopathic medicine (DO): a Canadian perspective
Bibliographic record
Abstract
Background: Doctors of osteopathic medicine (DO) are one of the fastest growing segments of health care professionals in the United States. Although Canada has taken significant leaps in the acknowledgment of US trained DOs, there continues to be a lack of understanding of the profession by Canadian trained physicians. In this article, we provide a brief overview of osteopathic medical education and training in the United States. Method: Current information of osteopathic training by American Association of Colleges of Osteopathic Medicine (AACOM) and American Osteopathic Association (AOA) was presented. Data pertaining to Canadians enrolled in osteopathic colleges was compared with allopathic (MD) and international medical graduates (IMGs). Results: Doctors of osteopathic medicine programs provide an additional pathway for students interested in pursuing a medical education. Canadian applications to osteopathic colleges are expected to grow due to successful post-graduate US residency matching, increased difficulty of matriculating at Canadian medical schools, and a greater awareness of the profession in Canada. Conclusions: Given the increasing enrollment of Canadian students in US osteopathic medical schools, we expect that Canadian DOs will play a significant role in shaping health care in both the US and Canada.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".