“It Was the Best Decision of My Life”: a thematic content analysis of former medical tourists’ patient testimonials
Bibliographic record
Abstract
BACKGROUND: Medical tourism is international travel with the intention of receiving medical care. Medical tourists travel for many reasons, including cost savings, limited domestic access to specific treatments, and interest in accessing unproven interventions. Medical tourism poses new health and safety risks to patients, including dangers associated with travel following surgery, difficulty assessing the quality of care abroad, and complications in continuity of care. Online resources are important to the decision-making of potential medical tourists and the websites of medical tourism facilitation companies (companies that may or may not be affiliated with a clinic abroad and help patients plan their travel) are an important source of online information for these individuals. These websites fail to address the risks associated with medical tourism, which can undermine the informed decision-making of potential medical tourists. Less is known about patient testimonials on these websites, which can be a particularly powerful influence on decision-making. METHODS: A thematic content analysis was conducted of patient testimonials hosted on the YouTube channels of four medical tourism facilitation companies. Five videos per company were viewed. The content of these videos was analyzed and themes identified and counted for each video. RESULTS: Ten main themes were identified. These themes were then grouped into three main categories: facilitator characteristics (e.g., mentions of the facilitator by name, reference to the price of the treatment or to cost savings); service characteristics (e.g., the quality and availability of the surgeon, the quality and friendliness of the support staff); and referrals (e.g., referrals to other potential medical tourists). These testimonials were found either not to mention risks associated with medical tourism or to claim that these risks can be effectively managed through the use of the facilitation company. The failure fully to address the risks of medical tourism can undermine the informed decision-making of potential medical tourists, particularly given the considerable influence on decision-making by patient testimonials. CONCLUSIONS: Regulation of these global companies is difficult, making the development of testimonials highlighting the risks of medical tourism essential. Additional research is needed on the impact of patient testimonial videos on the decision-making of potential medical tourists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.370 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".