COVID-19 Public Stigma Scale (COVID-PSS): development, validation, psychometric analysis and interpretation
Bibliographic record
Abstract
OBJECTIVE: Amid the COVID-19 pandemic, social stigma towards COVID-19 infection has become a major component of public discourse and social phenomena. As such, we aimed to develop and validate the COVID-19 Public Stigma Scale (COVID-PSS). DESIGN AND SETTING: National-based survey cross-sectional study during the lockdown in Thailand. PARTICIPANTS: We invited the 4004 adult public to complete a set of measurement tools, including the COVID-PSS, global fear of COVID-19, perceived risk of COVID-19 infection, Bogardus Social Distance Scale, Pain Intensity Scale and Insomnia Severity Index. METHODS: Factor structure dimensionality was constructed and reaffirmed with model fit by exploratory and confirmatory factor analyses and non-parametric item response theory (IRT) analysis. Psychometric properties for validity and reliability were tested. An anchor-based approach was performed for classifying the proper cut-off scores. RESULTS: After factor analysis, IRT analysis and test for model fit, we created the final 10-item COVID-PSS with a three-factor structure: stereotype, prejudice and fear. Face and content validity were established through the public and experts' perspectives. The COVID-PSS was significantly correlated (Spearman rank, 95% CI) with the global fear of COVID-19 (0.68, 95% CI 0.66 to 0.70), perceived risk of COVID-19 infection (0.79, 95% CI 0.77 to 0.80) and the Bogardus Social Distance Scale (0.50, 95% CI 0.48 to 0.53), indicating good convergent validity. The correlation statistics between the COVID-PSS and the Pain Intensity Scale and Insomnia Severity Index were <0.2, supporting the discriminant validity. The reliability of the COVID-PSS was satisfactory, with good internal consistency (Cronbach's α of 0.85, 95% CI 0.84 to 0.86) and test-retest reproducibility (intraclass correlation of 0.94, 95% CI 0.86 to 0.96). The proposed cut-off scores were as follows: no/minimal (≤18), moderate (19-25) and high (≥26) public stigma towards COVID-19 infection. CONCLUSIONS: The COVID-PSS is practical and suitable for measuring stigma towards COVID-19 in a public health survey. However, cross-cultural adaptation may be needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".