رفتار اطلاعیابی گردشگران سلولهای بنیادی براساس تحلیل گوگل ترندز
Bibliographic record
Abstract
Aim: Given the growing desire of people to obtain health information through the Internet, examining the web search process will provide valuable information to health professionals and policymakers. Analyzing the search terms of Google users can show their interests and tendencies. The present study studies the global trends in stem cell therapy in the last 5 years using Google Trends. In this research, the global search trends, the geographical distribution of search, the preferred search terms of users that can express the treatment preferences of treatment applicants, and the importance of treatment cost for potential treatment applicants have been considered.Methodology: This is an applied and analytical study and data were collected and analyzed using Google Trends. Google Trends is one of the services of the Google company through which the needs and behaviors of Google users can be analyzed. The results are from July 19, 2015, to July 12, 2020.Findings: The findings indicated that the search for "Stem cell therapy" has been an increasing trend in the period under review. Most searches were in the US, Singapore, and the Philippines. Also, the Asian countries of Bangladesh, Pakistan, India, Qatar, Malaysia, Kuwait, South Korea, and Hong Kong, the countries of Canada, New Zealand, England, and Ireland, and the African countries of Nigeria, Kenya, and South Africa are also among the 20 countries with the highest search volume index in the field of Stem cell therapy.Iran was ranked 25th in terms of the search volume index. Stem cell therapy for knee pain, pain, autism, arthritis, Mesenchymal stem cell therapy, and chronic obstructive pulmonary disease has attracted the attention of Google users. The results also indicate the importance of treatment costs for stem cell therapy in different countries. The most relevant searches in the field of treatment costs were conducted in the United States, Australia, India, Canada, and the United Kingdom.Conclusion: Google Trends is an effective tool for investigating the process of Google users searching for stem cell therapy. The data provided can be used to identify countries in which treatment is sought, treatment priorities, and treatment demand. Accordingly, appropriate policies can be adopted to attract stem cell tourists. In this regard, information specialists can use the Google Trends tool to extract user search data in various fields related to health and provide it to health experts and policymakers for future planning.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.031 |
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".