Identifying and Ranking Health Tourism Development Barriers in Iran Using Fuzzy VIKOR Method
Bibliographic record
Abstract
<p>The present research is an applied study which employed a descriptive-correlation method. After a review of the related literature and survey of opinions of health tourism industry experts, the considered criteria in evaluation and ranking health tourism barriers were determined. Subsequently, 4 criteria (price, quality, accessibility, and proper time) were selected as the most important criteria. Using purposeful sampling method, out of cities and regions with health tourism attraction, four cities were selected as the most important cities with health tourism attraction. Using Fuzzy VIKOR method, quality was found to be the highest importance and proper time was determined as the lowest important criterion. Among sub-indices, improper medical quality was found to have the highest importance (weight). Among the selected cities, Mazandaran was found to have the highest priority. Shiraz, Tabriz and Mashhad, then, had the highest importance, respectively, in terms of health tourism development barriers. </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".