RESEARCH Open Access Beyond “medical tourism”: Canadian companies of companies taking distinct approaches to marketing medical travel.
Bibliographic record
Abstract
Background: Despite having access to medically necessary care available through publicly funded provincial health care systems, some Canadians travel for treatment provided at international medical facilities as well as for-profit clinics found in several Canadian provinces. Canadians travel abroad for orthopaedic surgery, bariatric surgery, ophthalmologic surgery, stem cell injections, “Liberation therapy ” for multiple sclerosis, and additional interventions. Both responding to public interest in medical travel and playing an important part in promoting the notion of a global marketplace for health services, many Canadian companies market medical travel. Methods: Research began with the goal of locating all medical tourism companies based in Canada. Various strategies were used to find such businesses. During the search process it became apparent that many Canadian business promoting medical travel are not medical tourism companies. To the contrary, numerous types of businesses promote medical travel. Once businesses promoting medical travel were identified, content analysis was used to extract information from company websites. Company websites were analyzed to establish: 1) where in Canada these businesses are located; 2) the destination countries and health care facilities that they market; 3) the medical procedures they promote; 4) core marketing messages; and 5) whether businesses market air travel, hotel accommodations, and holiday tours in addition to medical procedures.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".