“Medical tourism will…obligate physicians to elevate their level so that they can compete”: a qualitative exploration of the anticipated impacts of inbound medical tourism on health human resources in Guatemala
Bibliographic record
Abstract
BACKGROUND: Medical tourism, which involves cross-border travel to access private, non-emergency medical interventions, is growing in many Latin American Caribbean countries. The commodification and export of private health services is often promoted due to perceived economic benefits. Research indicates growing concern for health inequities caused by medical tourism, which includes its impact on health human resources, yet little research addresses the impacts of medical tourism on health human resources in destination countries and the subsequent impacts for health equity. To address this gap, we use a case study approach to identify anticipated impacts of medical tourism sector development on health human resources and the implications for health equity in Guatemala. METHODS: After undertaking an extensive review of media and policy discussions in Guatemala's medical tourism sector and site visits observing first-hand the complex dynamics of this sector, in-depth key informant interviews were conducted with 50 purposefully selected medical tourism stakeholders in representing five key sectors: public health care, private health care, health human resources, civil society, and government. Participants were identified using multiple recruitment methods. Interviews were transcribed in English. Transcripts were reviewed to identify emerging themes and were coded accordingly. The coding scheme was tested for integrity and thematic analysis ensued. Data were analysed thematically. RESULTS: Findings revealed five areas of concern that relate to Guatemala's nascent medical tourism sector development and its anticipated impacts on health human resources: the impetus to meet international training and practice standards; opportunities and demand for English language training and competency among health workers; health worker migration from public to private sector; job creation and labour market augmentation as a result of medical tourism; and the demand for specialist care. These thematic areas present opportunities and challenges for health workers and the health care system. CONCLUSION: From a health equity perspective, the results question the responsibility of Guatemala's medical education system for supporting an enhanced medical tourism sector, particularly with an increasing focus on the demand for private clinics, specific specialities, English-language training, and international standards. Further, significant health inequalities and barriers to care for Indigenous populations are unlikely to benefit from the impacts identified from participants, as is true for rural-urban and public-private health human resource migration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".