Medical Tourism in Guatemala: Qualitatively Exploring How Existing Health System Inequities Facilitate Sector Development
Bibliographic record
Abstract
This article explores how existing health inequities in the Guatemalan health system facilitate the emergence of its medical tourism industry. We report on our thematic analysis of 50 key informant interviews conducted with 4 groups of stakeholders in the local medical tourism sector. Participants frequently discussed the interplay between the country’s longstanding health inequities and the promotion of medical tourism, characterized by 4 thematic viewpoints: the private health sector is already flourishing; the highly fragmented health system already faces multiple challenges; the underfunded public health sector has a weak regulatory capacity; and the commodification of health care has already advanced. Medical tourism and health inequities shape each other in low- and middle-income countries. In addition to the potential for medical tourism to exacerbate health inequities, previously existing health inequities create opportunities for the industry’s growth. Although regulation of the medical tourism industry is necessary, it needs to be implemented both at the domestic and supranational levels for it to be effective in preventing greater health inequities, and it needs to address the political and economic drivers that make health systems generate health disparities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".