“Best care on home ground” versus “elitist healthcare”: concerns and competing expectations for medical tourism development in Barbados
Bibliographic record
Abstract
INTRODUCTION: Many countries have demonstrated interest in expanding their medical tourism sectors because of its potential economic and health system benefits. However, medical tourism poses challenges to the equitable distribution of health resources between international and local patients and private and public medical facilities. Currently, very little is known about how medical tourism is perceived among front line workers and users of health systems in medical tourism 'destinations'. Barbados is one such country currently seeking to expand its medical tourism sector. Barbadian nurses and health care users were consulted about the challenges and benefits posed by ongoing medical tourism development there. METHODS: Focus groups were held with two stakeholder groups in May, 2013. Nine (n = 9) citizens who use the public health system participated in the first focus group and seven (n = 7) nurses participated in the second. Each focus group ran for 1.5 hours and was digitally recorded. Following transcription, thematic analysis of the digitally coded focus group data was conducted to identify cross-cutting themes and issues. RESULTS: Three core concerns regarding medical tourism's health equity impacts were raised; its potential to 1) incentivize migration of health workers from public to private facilities, 2) burden Barbados' lone tertiary health care centre, and 3) produce different tiers of quality of care within the same health system. These concerns were informed and tempered by the existing a) health system structure that incorporates both universal public healthcare and a significant private medical sector, b) international mobility among patients and health workers, and c) Barbados' large recreational tourism sector, which served as the main reference in discussions about medical tourism's impacts. Incorporating these concerns and contextual influences, participants' shared their expectations of how medical tourism should locally develop and operate. CONCLUSIONS: By engaging with local health workers and users, we begin to unpack how potential health equity impacts of medical tourism in an emerging destination are understood by local stakeholders who are not directing sector development. This further outlines how these groups employ knowledge from their home context to ground and reconcile their hopes and concerns for the impacts posed by medical tourism.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".