The Impact of Medical Tourism on Thai Private Hospital Management: Informing Hospital Policy
Bibliographic record
Abstract
BACKGROUND: The purpose of this paper is to help consolidate and understand management perceptions and experiences of a targeted group (n=7) of Vice-Presidents of international Private Thai hospitals in Bangkok regarding medical tourism impacts. METHODS: The method adopted uses a small-scale qualitative inquiry. Examines the on-going development and service management factors which contribute to the establishment and strengthening of relationships between international patients and hospital medical services provision. Develops a qualitative model that attempts to conceptualize the findings from a diverse range of management views into a framework of main (8) - Hospital Management; Hospital Processes; Hospital Technology; Quality Related; Communications; Personnel; Financial; and Patients; and consequent sub-themes (22). RESULTS: Outcomes from small-scale qualitative inquiries cannot by design be taken outside of its topical arena. This inevitably indicates that more research of this kind needs to be carried out to understand this field more effectively. The evidence suggests that Private Thai hospital management have established views about what constitutes the impact of medical tourism on hospital policies and practices when hospital staff interact with international patients. CONCLUSIONS: As the private health service sector in Thailand continues to grow, future research is needed to help hospitals provide appropriate service patterns and appropriate medical products/services that meet international patient needs and aspirations. Highlights the increasing importance of the international consumer in Thailand's health industry. This study provides insights of private health service providers in Bangkok by helping to understand more effectively health service quality environments, subsequent service provision, and the integrated development and impacts of new medical technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".