Policy implications of medical tourism development in destination countries: revisiting and revising an existing framework by examining the case of Jamaica
Bibliographic record
Abstract
BACKGROUND: Medical tourism is now targeted by many hospitals and governments worldwide for further growth and investment. Southeast Asia provides what is perhaps the best documented example of medical tourism development and promotion on a regional scale, but interest in the practice is growing in locations where it is not yet established. Numerous governments and private hospitals in the Caribbean have recently identified medical tourism as a priority for economic development. We explore here the projects, activities, and outlooks surrounding medical tourism and their anticipated economic and health sector policy implications in the Caribbean country of Jamaica. Specifically, we apply Pocock and Phua's previously-published conceptual framework of policy implications raised by medical tourism to explore its relevance in this new context and to identify additional considerations raised by the Jamaican context. METHODS: Employing case study methodology, we conducted six weeks of qualitative fieldwork in Jamaica between October 2012 and July 2013. Semi-structured interviews with health, tourism, and trade sector stakeholders, on-site visits to health and tourism infrastructure, and reflexive journaling were all used to collect a comprehensive dataset of how medical tourism in Jamaica is being developed. Our analytic strategy involved organizing our data within Pocock and Phua's framework to identify overlapping and divergent issues. RESULTS: Many of the issues identified in Pocock and Phua's policy implications framework are echoed in the planning and development of medical tourism in Jamaica. However, a number of additional implications, such as the involvement of international development agencies in facilitating interest in the sector, cyclical mobility of international health human resources, and the significance of health insurance portability in driving the growth of international hospital accreditation, arise from this new context and further enrich the original framework. CONCLUSIONS: The framework developed by Pocock and Phua is a flexible common reference point with which to document issues raised by medical tourism in established and emerging destinations. However, the framework's design does not lend itself to explaining how the underlying health system factors it identifies work to facilitate medical tourism's development or how the specific impacts of the practice are likely to unfold.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".