The Impact of Macroeconomic Indicators on Medical Tourism: A Global Perspective
Bibliographic record
Abstract
Medical tourism is an expanding global phenomenon stimulating economic development driven by a combination of multiple macro and socioeconomic factors. The study aims to investigate whether the development of medical tourism globally is associated with specific macroeconomic indicators. The association of Medical Tourism Index (MTI) is explored with Gross Domestic Product (GDP), healthcare spending and international tourism receipts. A countries’ grouping method and an ordinary least squares (OLS) regression model were used. MTI was used as the outcome variable, while macroeconomic indicators as potential predictors. The grouping of countries showed that European and Asian countries as well as Canada excel high ranking in all indicators. However, the Gulf Cooperation Council countries (GCC) that exhibit high performance in GDP, rank below the MTI average. Caribbean and Latin American countries are ranking high in the MTI but fall below the average in all the indicators. The OLS analysis showed a positive correlation between the score of the MTI and health expenditure and no correlation among MTI, GDP and international tourism receipts, a finding also supported by the grouping analysis. Taking into consideration the similarities and disparities found among countries regarding the macroeconomic indicators mostly impacting medical tourism, it seems that there is a consensus on their relationship but not clearly identified rather due to a variety of social, cultural and ethical factors that dominate in each country. Further research is needed in order to obtain more robust and comparative evidence.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".