What do we know about Canadian involvement in medical tourism?: a scoping review.
Bibliographic record
Abstract
BACKGROUND: Medical tourism, the intentional pursuit of elective medical treatments in foreign countries, is a rapidly growing global industry. Canadians are among those crossing international borders to seek out privately purchased medical care. Given Canada's universally accessible, single-payer domestic health care system, important implications emerge from Canadians' private engagement in medical tourism. METHODS: A scoping review was conducted of the popular, academic, and business literature to synthesize what is currently known about Canadian involvement in medical tourism. Of the 348 sources that were reviewed either partly or in full, 113 were ultimately included in the review. RESULTS: The review demonstrates that there is an extreme paucity of academic, empirical literature examining medical tourism in general or the Canadian context more specifically. Canadians are engaged with the medical tourism industry not just as patients but also as investors and business people. There have been a limited number of instances of Canadians having their medical tourism expenses reimbursed by the public medicare system. Wait times are by far the most heavily cited driver of Canadians' involvement in medical tourism. However, despite its treatment as fact, there is no empirical research to support or contradict this point. DISCUSSION: Although medical tourism is often discussed in the Canadian context, a paucity of data on this practice complicates our understanding of its scope and impact.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.048 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".