Understanding the behaviour of medical tourists: implications for strategy development
Bibliographic record
Abstract
Strategy and policy development for medical tourism largely depends on knowledge and understanding of the behavioral intentions of patients for cross-border travel to seek healthcare services. This study utilizes the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and decision tree models to explore the behavioral intentions of medical tourists and identify key factors for predicting medical tourism adoption decisions by patients. Safety expectancy and waiting time are found to be the most influential features for the prediction of behavioral intention and adoption behavior. Though social influence and price-value are found to be very important in predicting behavioral intentions, these features become redundant in predicting medical tourism adoption behavior. This study also reveals that medical tourists rarely consider recreational benefits as a supplementary service besides health services; rather, they decide to pursue medical services based on the primary healthcare service itself. This finding can provide deep knowledge to develop policies and strategies for medical tourism. HighlightsAn expanded conceptual framework is proposed to explore medical tourism.Six major factors emerged as determinants of tourists’ behavioral intentions.Impact of effort expectancy on behavioral intention is insignificant.Safety expectancy and waiting time are dominant predictors of behavioral intention.No significant impact of hedonic urge on medical tourism adoption behaviour.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".