A Study of Sociocultural Adaptation for Practices of Religious Doctrines during the Covid-19 Pandemic and Using the Religious Dimension to Prevent and Mitigate the Covid-19 Pandemic
Bibliographic record
Abstract
This article presents the findings of a study on the effects of sociocultural adaptation on religious practices during the COVID-19 pandemic and the utilization of the religious dimension to prevent and mitigate the spread of the virus. The results stem from a mixed-methods research approach involving quantitative and qualitative data collection from 15,505 participants through questionnaires. Statistical analysis comprises numerical summaries such as counts, percentages, means, standard deviations, and qualitative content analysis. Data presentation employed descriptive analytics. Findings revealed: 1) Among Thai followers, a high adherence to religious practices was observed (94.62%). Brahmanism-Hinduism adherents demonstrated the highest adherence (100.00%), followed by Sikhs (94.60%), Muslims (93.60%), Buddhists (92.50%), and Christians (92.43%). 2) Sociocultural adaptation for religious practices during the pandemic aligned with public health guidelines, emphasizing social distancing, mask-wearing, and frequent hand sanitization. 3) Clear directives were established for utilizing religious practices to combat COVID-19, ensuring conformity with the Ministry of Public Health protocols for all religious ceremonies, both auspicious and inauspicious. Additionally, education played a vital role in disseminating these guidelines effectively to religious communities, fostering greater compliance and understanding.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".