What is known about the effects of medical tourism in destination and departure countries? A scoping review
Bibliographic record
Abstract
BACKGROUND: Medical tourism involves patients intentionally leaving their home country to access non-emergency health care services abroad. Growth in the popularity of this practice has resulted in a significant amount of attention being given to it from researchers, policy-makers, and the media. Yet, there has been little effort to systematically synthesize what is known about the effects of this phenomenon. This article presents the findings of a scoping review examining what is known about the effects of medical tourism in destination and departure countries. METHODS: Drawing on academic articles, grey literature, and media sources extracted from18 databases, we follow a widely used scoping review protocol to synthesize what is known about the effects of medical tourism in destination and departure countries. The review design has three main stages: (1) identifying the question and relevant literature; (2) selecting the literature; and (3) charting, collating, and summarizing the data. RESULTS: The large majority of the 203 sources accepted into the review offer a perspective of medical tourism from the Global North, focusing on the flow of patients from high income nations to lower and middle income countries. This greatly shapes any discussion of the effects of medical tourism on destination and departure countries. Five interrelated themes that characterize existing discussion of the effects of this practice were extracted from the reviewed sources. These themes frame medical tourism as a: (1) user of public resources; (2) solution to health system problems; (3) revenue generating industry; (4) standard of care; and (5) source of inequity. It is observed that what is currently known about the effects of medical tourism is minimal, unreliable, geographically restricted and mostly based on speculation. CONCLUSIONS: Given its positive and negative effects on the health care systems of departure and destination countries, medical tourism is a highly significant and contested phenomenon. This is especially true given its potential to serve as a powerful force for the inequitable delivery of health care services globally. It is recommended that empirical evidence and other data associated with medical tourism be subjected to clear and coherent definitions, including reports focused on the flows of medical tourists and surgery success rates. Additional primary research on the effects of medical tourism is needed if the industry is to develop in a manner that is beneficial to citizens of both departure and destination countries.
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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 | 0.022 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".