“I didn’t even know what I was looking for”: A qualitative study of the decision-making processes of Canadian medical tourists
Bibliographic record
Abstract
BACKGROUND: Medical tourism describes the private purchase and arrangement of medical care by patients across international borders. Increasing numbers of medical facilities in countries around the world are marketing their services to a receptive audience of international patients, a phenomenon that has largely been made possible by the growth of the Internet. The growth of the medical tourism industry has raised numerous concerns around patient safety and global health equity. In spite of these concerns, there is a lack of empirical research amongst medical tourism stakeholders. One such gap is a lack of engagement with medical tourists themselves, where there is currently little known about how medical tourists decide to access care abroad. We address this gap through examining aspects of Canadian medical tourists' decision-making processes. METHODS: Semi-structured phone interviews were administered to 32 Canadians who had gone abroad as medical tourists. Interviews touched on motivations, assessment of risks, information seeking processes, and experiences at home and abroad. A thematic analysis of the interview transcripts followed. RESULTS: Three overarching themes emerged from the interviews: (1) information sources consulted; (2) motivations, considerations, and timing; and (3) personal and professional supports drawn upon. Patient testimonials and word of mouth connections amongst former medical tourists were accessed and relied upon more readily than the advice of family physicians. Neutral, third-party information sources were limited, which resulted in participants also relying on medical tourism facilitators and industry websites. CONCLUSIONS: While Canadian medical tourists are often thought to be motivated by wait times for surgery, cost and availability of procedures were common primary and secondary motivations for participants, demonstrating that motivations are layered and dynamic. The findings of this analysis offer a number of important factors that should be considered in the development of informational interventions targeting medical tourists. It is likely that trends observed amongst Canadian medical tourists apply to those from other nations due to the key role the transnational medium of the Internet plays in facilitating patients' private international medical travel.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".