RESEARCH ARTICLE Open Access An industry perspective on Canadian patients’ involvement in Medical Tourism: implications for public health
Bibliographic record
Abstract
Background: The medical tourism industry, which assists patients with accessing non-emergency medical care abroad, has grown rapidly in recent years. A lack of reliable data about medical tourism makes it difficult to create policy, health system, and public health responses to address the associated risks and shortcomings, such as spread of infectious diseases, associated with this industry. This article addresses this knowledge gap by analyzing interviews conducted with Canadian medical tourism facilitators in order to understand Canadian patients’ involvement in medical tourism and the implications of this involvement for public health. Methods: Semi-structured phone interviews were conducted with 12 medical facilitators from 10 companies in 2010. An exhaustive recruitment strategy was used to identify interviewees. Questions focused on business dimensions, information exchange, medical tourists ’ decision-making, and facilitators ’ roles in medical tourism. Thematic analysis was undertaken following data collection. Results: Facilitators helped their Canadian clients travel to 11 different countries. Estimates of the number of clients sent abroad annually varied due to demand factors. Facilitators commonly worked with medical tourists aged between 40 and 60 from a variety of socio-economic backgrounds who faced a number of potential barriers including affordability, fear of the unfamiliar, and lack of confidence. Medical tourists who chose not to use
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".