Determining and Prioritizing Factors Affecting to Increase Customers Attraction of Medical Tourism from the Perspective of Arabic Countries (Case Study: Iran-Mashhad Razavi Hospital)
Bibliographic record
Abstract
Today, combination of medicine and tourism has turned into a new form of industry called health tourism. Health tourism industry has experienced a dramatic growth over the last decade. This industry is an opportunity for hospitals to extend their services to patients from other countries. The present study aims to identify factors contributing to the attraction of medical tourism along with ranking factors affecting customer satisfaction of Persian Gulf Countries .For this purpose, after studying the existing literature and matching the resulting data with the environmental conditions of the study six factors “the expertise and skill level of the hospital staff”, “hospital facilities and equipments”, “costs”, “the way patients are treated by the staff and their relationship”, “shared beliefs and values”, “tourist and travel facilities” were determined and based on them questionnaires were designed and distributed to a number of patients of Persian Gulf Countries as well as the managers of the Razavi Hospital in Mashhad, where specialized medical tourism services are provided .Investigations indicated that from the viewpoint of both managers and patients, the most important factor affecting the attraction of the medical tourism of Razavi Hospital in Mashhad is the costs. These costs consist of medical expenses (such as hospital, medicine and tests costs), costs of travel and accommodation (such as hotel and guesthouse), transportation costs within the city and side expenditures (including visiting pilgrimage places and entertainment spots). Therefore, lowering the costs of medical treatments can be a strategy that the hospitals can benefit from in order to enter the medical tourism market.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".