What Canadian Family Physicians Need to Know About Medical Tourism
Bibliographic record
Abstract
Broadly speaking, medical tourism involves patients intentionally going abroad to pursue medical services outside of formal cross-border care arrangements that are typically paid for out-of-pocket. Orthopedic, dental, cosmetic, transplant, and other surgeries are offered by hospitals around the world looking to attract international patients, with such procedures often available for purchase as part of “package deals” that include recovery stays at affiliated tourist resorts or hotels. In this commentary we synthesize what we believe are the 10 most important issues of concern for Canadian family physicians regarding Canadian patients’ involvement in medical tourism. In effect, our intent is to reignite discussion on the relevance of medical tourism to Canadian family medicine that was started by the 2007 commentary by Leigh Turner (Can Fam Physician 2007;53:1639-41) and to use this as an opportunity to inform Canadian family physicians about key issues of current concern. We believe it is particularly timely to reignite discussion about medical tourism in the Canadian context given recent reports of a new “super-bug” (NDM-1 [New Delhi metallo-beta-lactamase]) having been contracted by some Canadian medical tourists who underwent surgery in India in 2010.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 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".