A critical examination of empowerment discourse in medical tourism: the case of the dental tourism industry in Los Algodones, Mexico
Bibliographic record
Abstract
BACKGROUND: Medical tourism is a term used to describe the phenomenon of individuals intentionally traveling across national borders to privately purchase medical care. The medical tourism industry has been portrayed in the media as an "escape valve" providing alternative care options as a result of vast economic asymmetries between the global north and global south and the flexible regulatory environment in which care is provided to medical tourists. Discourse suggesting the medical tourism industry necessarily enhances access to medical care has been employed by industry stakeholders to promote continued expansion of the industry; however, it remains unknown how this discourse informs industry practices on the ground. Using case study methodology, this research examines the perspectives and experiences of industry stakeholders working and living in a dental tourism industry site in northern Mexico to develop a better understanding of the ways in which common discourses of the industry are taken up or resisted by various industry stakeholders and the possible implications of these practices on health equity. RESULTS: Interview discussions with a range of industry stakeholders suggest that care provision in this particular location enables international patients to access high quality dental care at more affordable prices than typically available in their home countries. However, interview participants also raised concerns about the quality of care provided to medical tourists and poor access to needed care amongst local populations. These concerns disrupt discourses about the positive health impacts of the industry commonly circulated by industry stakeholders positioned to profit from these unjust industry practices. CONCLUSIONS: We argue in this paper that elite industry stakeholders in our case site took up discourses of medical tourism as enhancing access to care in ways that mask health equity concerns for the industry and justify particular industry activities despite health equity concerns for these practices. This research provides new insight into the ways in which the medical tourism industry raises ethical concern and the structures of power informing unethical practices.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.024 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".