“They don’t have the history and the stature:” examining perceptions of Caribbean offshore medical schools held by Canadian medical education stakeholders
Bibliographic record
Abstract
BACKGROUND: Caribbean offshore medical schools are for-profit, private institutions that provide undergraduate medical education to primarily international students, including from the United States or Canada. Despite the growing role that offshore medical schools play in training Canadian physicians, little is known about how these institutions are perceived by those in professional and decision-making positions where graduates intend to practice. METHODS: The authors interviewed 13 Canadian medical education stakeholders whose professional positions entail addressing the medical education system or physician workforce. Participants were employed in academic, governmental, and non-governmental organizations in leadership roles. RESULTS: Thematic analysis revealed three cross-cutting perceptions of offshore medical schools: (a) they are at the bottom of an international hierarchy of medical schools; (b) they are heterogeneous in quality of education and student body; and (c) they have a unique business model, characterized by profit-generating and serving international students. CONCLUSION: Consistent growth of the offshore medical school industry in the Caribbean may result in adverse reputational harms for well-established offshore or regional medical schools. Both comparative (e.g., USMLE pass rate) and intuitive factors (e.g., professional familiarity) informed participants' perceptions. Participants believed that core principles of social accountability in medical education are incompatible with the offshore medical school model.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".