Medical Tourism Challenges After the Prevalence of COVID-19: The Neurosurgery Field
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has widely affected medical tourism on a global scale, thereby reducing the number and volume of medical services. Given the importance of this topic, the present study aimed to determine the challenges of medical tourism after the prevalence of COVID-19 in the field of neurosurgery. Methods: The present descriptive study was conducted by the neurosurgery department of Isfahan University of Medical Sciences, Isfahan, Iran in the first quarter of 2022. Using the convenience sampling method and based on Morgan’s table, 500 patients with neurosurgical diseases registered in Medical Tourism companies were identified and included in the study. The data were analyzed in SPSS. Results: 142 (28.4%) out of 500 patients with COVID-19 were willing to come to Iran for neurosurgical treatment. The most important non-medical reasons included natural attractions (4.37±0.44), cost-effective accommodation (4.03±0.23), and support from a country of destination (place of residence) (3.75±0.22). The most important medical reasons included the short waiting list, the fast treatment response (4.26±0.76), the availability of qualified doctors (3.96±0.27), and the low-cost treatment (3.87±0.53). Conclusion: The present study focused on the functions and potentials of medical tourism in neurosurgery. It can be more successful by providing the right conditions to improve the current situation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".