Disparities and Factors Associated with Coronavirus Disease-2019-Related Public Stigma: A Cross-Sectional Study in Thailand
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19)-related public stigma is a major challenge, with scarce available evidence. This study aimed to determine the disparities and factors associated with COVID-19-related public stigma in the Thai population. We conducted a cross-sectional study involving a voluntary online survey in Thailand from 21 April 2020 to 4 May 2020. We invited 4004 participants to complete a series of questionnaires, including the validated COVID-19 public stigma scale and questions on relevant COVID-19-related psychosocial issues. Multinomial logistic regression was performed to investigate the factors associated with COVID-19-related public stigma. The prevalence of COVID-19-related public stigma was 24.2% (95% confidence interval [CI], 22.2-26.2) for no/minimal, 35.5% (95% CI, 33.4-37.6) for moderate, and 40.3% (95% CI, 38.2-42.4) for high. We observed disparities in the prevalence of COVID-19-related public stigma according to participant characteristics and psychosocial factors. Using the no/minimal group as a reference group, the six predominant risk factors significantly associated with a moderate and high degree of COVID-19-related public stigma were middle-aged or older adults, male, divorced/widowed/separated, current quarantine status, moderate/severe fear of COVID-19, and medium/high perceived risk of COVID-19. Additional risk factors significantly related to a high degree of COVID-19-related public stigma were religion (Buddhist), region of residence (non-capital city), and exposure to COVID-19-related information. Disparities in COVID-19-related public stigma due to sociodemographic and psychosocial issues are frequent in the Thai population. To reduce public stigmatization, early identification of vulnerable groups and the development of tailored mitigation strategies should be implemented during the pandemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".