Population-level interest and trends in meditation and yoga during lockdown imposed due to coronavirus disease 2019 pandemic In India
Bibliographic record
Abstract
Introduction: Yoga and meditation have a potential to give mental peace and calm. The present coronavirus disease 2019 (COVID-19) has forced countries to impose lockdown due to its infectious nature, thus restricting people in their homes posing psychosocial impact which can be reduced through yoga. Google Trends (GT) is a proxy indicator for population-level interests, which is used instead of traditional survey methods during pandemic. The objective of this study was to monitor population-level interest and trends in yoga and meditation during lockdown imposed due to COVID-19 in India through GT. Material & Methods: GT is an open-access, web-based tool which provides unfiltered sample of active search requests made to Google. Various keywords related to yoga and meditation were used to retrieve web-based search volume from January 30, 2020, to June 7, 2020, for India. These data were correlated with number of cases and deaths reported due to COVID-19 as an increase in cases and death might lead to stress among masses. Results: The search trends and daily number of confirmed cases were fairly correlated ( r = 0.647, P = 0.000). The relative search volume for the search trends was also fairly correlated ( r = 0.665, P = 0.000) with number of daily deaths due to COVID-19. States such as Uttarakhand and Goa had a higher share of search whereas Meghalaya and West Bengal searched the least. Conclusion: GT showed an increase in population-level interest in yoga and meditation during COVID-19 lockdown which is a positive indicator for population. This indicates the need for continuity of trend so as to make it a routine habit even after the situation becomes normal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".