Web-based medical facilitators in medical tourism: the third party in decision-making
Bibliographic record
Abstract
Background: Medical tourism is understood as travel abroad with the intention of obtaining non-emergency medical services.This practice is the subject of increasing interest, but little is known about its scope.Methods: A comprehensive scoping review of published academic articles, media sources, and grey literature reports was performed to answer the question: what is known about the patient's experience of medical tourism?The review was accomplished in three steps: (1) identifying the question and relevant literature; (2) selecting the literature; (3) charting, collating, and summarizing the information.Overall themes were identified from this process.Results: 291 sources were identified for review from the databases searched, the majority of which were media pieces (n = 176).A further 57 sources were included for review after hand searching reference lists.Of the 348 sources that were gathered, 216 were ultimately included in this scoping review.Only a small minority of sources reported on empirical studies that involved the collection of primary data (n = 5).The four themes identified via the review were: (1) decision-making (e.g., push and pull factors that operate to shape patients' decisions); (2) motivations (e.g., procedure-, cost-, and travel-based factors motivating patients to seek care abroad); (3) risks (e.g., health and travel risks); and (4) first-hand accounts (e.g., patients' experiential accounts of having gone abroad for medical care).These themes represent the most discussed issues about the patient's experience of medical tourism in the English-language academic, media, and grey literatures.Conclusions: This review demonstrates the need for additional research on numerous issues, including: (1) understanding how multiple information sources are consulted and evaluated by patients before deciding upon medical tourism; (2) examining how patients understand the risks of care abroad; (3) gathering patients' prospective and retrospective accounts; and (4) the push and pull factors, as well as the motives of patients to participate in medical tourism.The findings from this scoping review and the knowledge gaps it uncovered also demonstrate that there is great potential for new contributions to our understanding of the patient's experience of medical tourism.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".