A COMMENTARY ON SHIFT IN BUSINESS STRATEGIES OF INDIAN HEALTH CARE INDUSTRY WITH COVID-19 AS A TRIGGER
Bibliographic record
Abstract
Medical tourism has become a booming industry in the recent past. People from all around the world cross the borders for better medical treatment. The leading destinations with markets for medical tourism include Malaysia, Thailand, India, Singapore, Turkey, and United States. Latest medical technology, high-quality services, insurance are a few of the criteria medical tourists seek for. As public-funded well-being insurance is unable to keep pace with the increasing demands of a growing aging population, patients from the United Kingdom and Canada travel to India to beat the huge waiting period for the routine procedures. The unprecedented COVID-19 outbreak has forced the market to observe diminishing growth. The pandemic is predicted to have a negative impact on this growing industry. The organizations, involved in the development of the medical tourism, stare at a dark future. It is, therefore, necessary to streamline the industry in view of this dismal scenario. However, with the growing technological development, one such platform that can bridge the distance in the health sector is telemedicine. This paper is an attempt to study the growing importance of telemedicine in a developing country like India. The research is based on both primary and secondary data along with a thorough literature review. Post lockdown telemedicine is likely to grow, and telemedicine is probably the future of the healthcare industry.
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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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.050 | 0.059 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".