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Record W3209270832 · doi:10.1136/bmjopen-2020-048241

COVID-19 Public Stigma Scale (COVID-PSS): development, validation, psychometric analysis and interpretation

2021· article· en· W3209270832 on OpenAlexaff
Surapon Nochaiwong, Chidchanok Ruengorn, Ratanaporn Awiphan, Penkarn Kanjanarat, Yongyuth Ruanta, Chabaphai Phosuya, Waraporn Boonchieng, Sirisak Nanta, Wilaiwan Chongruksut, Kednapa Thavorn, Nahathai Wongpakaran, Tinakon Wongpakaran

Bibliographic record

VenueBMJ Open · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsInstitute for Clinical Evaluative SciencesOttawa HospitalUniversity of Ottawa
FundersChiang Mai University
KeywordsConfirmatory factor analysisSocial distanceMedicineExploratory factor analysisConvergent validityPublic healthSocial stigmaClinical psychologyScale (ratio)PsychometricsCriterion validityItem response theoryPsychologyCoronavirus disease 2019 (COVID-19)StatisticsStructural equation modelingConstruct validityFamily medicinePathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.233
GPT teacher head0.527
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations44
Published2021
Admission routes1
Has abstractyes

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