Bibliographic record
Abstract
Background and Aim : The high cost of care and long waiting time of the patients caused creation of the motivation that patients, especially from the developed countries, receive these services in other countries. On the other hand, also in developing countries, factors such as globalization and liberalization of trade in services, led the way for the rapid growth of tourism. However, these industry is facing a number of challenges, among the most important, can be noted. Since little research has been done in this regard, the aim of this study is to evaluate the related to tourism. Materials and Methods : This study is a review article on the issues of tourism. In order to collect information on the issues of databases, SID and PubMed, Google Scholar were examined using the key words medical tourism, health and legal aspects and about 47 related articles were found, after studying the titles and abstracts of the articles found, 23 subjects which were associated with the research topic, were studied. Then the results obtained were analyzed. Ethical Considerations : Honesty and integrity were taken into consideration in searching, analyzing, and reporting the texts. Findings : According to the results of the study, tourism industry is growing, but there are challenges related to the of tourism include the areas of access to services, errors, insurances, licensing and regulatory approvals of centers, operations and technologies. Conclusion : Medical Tourism challenges should be taken into consideration by policy makers and trustee institutions in this industry, as according to globalization of these challenges, trying to eliminate them is necessary, which by turning threats into opportunities, facilitates the attraction process of foreign patients. Citation: Abualhasani N. Legal Features and Aspects of Medical Tourism. Bioeth Health Law J . 2017; 1(1):35-39.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".