Global Change in Interest toward Yoga for Mental Health Ailments during Coronavirus Disease-19 Pandemic
Bibliographic record
Abstract
Background: With coronavirus disease (COVID)-19 pandemic, society is gripped with uncertainty and fear, inclining them toward Yoga to prevent mental health issues. Google Trends (GT) depicts the public interest of the community which may vary due to evolving policy dynamics of the COVID-19 pandemic. Aim: The aim was to study global public interest in Yoga for mental health during the COVID-19 pandemic. Material and Methods: Global time trends were obtained for Yoga, Anxiety, and Depression from November 1, 2019 to May 31, 2020 using GT. The time series analysis was done in three different time periods – pre-COVID-19 phase, transition period, and COVID-19 pandemic phase. Cross-correlation, Spearman rho, Friedman ANOVA test, and forecasting were used for analysis. Results: GT found a global change in the search queries for Yoga, anxiety, and depression during the three time periods. High burden COVID-19 countries – Italy, Spain, Russia, and Brazil had an increasing search trend for Yoga. During the COVID-19 phase, there was a significant positive correlation between the search trends of Yoga with depression ( r = 0.232; P < 0.05) and anxiety ( r = 0.351; P < 0.05), but higher anxiety and depression searches lead to lower Yoga searches at lag +6. Forecast projected a continuous increase in Yoga searches and anxiety queries. Conclusion: Google Trends captured a significant rise in interest of Yoga among the global community but diminished with time. Hence, the need for interventions to promote Yoga to be part of routine life and for making sure that people adhere to the Yoga practices on a continuous basis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".