Government roles in regulating medical tourism: evidence from Guatemala
Bibliographic record
Abstract
BACKGROUND: Regulation of the medical tourism and public health sectors overlap in many instances, raising questions of how patient safety, economic growth, and health equity can be protected. The case of Guatemala is used to explore how the regulatory challenges posed by medical tourism should be dealt with in countries seeking to grow this sector. METHODS: We conducted a qualitative case study of the medical tourism sector in Guatemala, through reviews and analyses of policy documents and media reports, key informant interviews (n = 50), and facility site-visits. RESULTS: Key informants were critical of the absence of effective public regulation of the emerging medical tourism sector, noting several regulatory gaps and the importance of filling them. These informants specifically expressed that: 1) The government should regulate medical tourism in Guatemala, thought there was disagreement as to which government sector should do so and how; 2) The government has not at this time regulated the medical tourism sector nor shown great interest in doing so; and 3) International accreditation could be used to augment domestic regulation. CONCLUSIONS: The intersection of domestic and international regulation of medical tourism has been largely unexplored. This case study advances new research in this area. It highlights the need for and dearth of regulatory protections in Guatemala and lessons for other, similarly situated countries. National regulatory models from Israel and Barbados could be adapted to the Guatemalan context. Global governance could help to protect national governments from any competitive disadvantages created by regulation. Underlying the concerns over growth in medical tourism, however, is how it contributes to the ongoing privatization of health care facilities worldwide. This trend risks undermining efforts to reach targets for Universal Health Coverage and exacerbating existing inequities in the global distribution of health and wealth.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".