Validity and reliability of the COVID‐19 Anxiety Syndrome Scale in Canadian dentists
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has resulted in a high level of mental health problems for the population worldwide including healthcare workers. Several studies have assessed these using measurements for anxiety for general populations. The COVID-19 Anxiety Syndrome Scale (C-19ASS) is a self-report measure developed to assess maladaptive forms of coping with COVID-19 (avoidance, threat monitoring and worry) among a general adult population in the United States. We used it in a prospective cohort study of COVID-19 incidence rates in practising Canadian dentists. We therefore need to ensure that it is valid for dentists in French and English languages. This study aimed to evaluate the validity of the C-19ASS in that population. METHODS: Cross-sectional data from the January 2021 monthly follow-up in our prospective cohort study were used. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed. RESULTS: The results of EFA revealed a 2-factor structure solution that explained 47% of the total variance. The CFA showed a good model fit on the data in both English and French languages. The Cronbach's alpha indicated acceptable levels of reliability. Furthermore, the C-19ASS showed excellent divergent validity from the Generalized Anxiety Disorder-7 (GAD-7) scale. CONCLUSIONS: The C-19ASS is valid and reliable instrument to measure COVID-19-related anxiety in English and French among Canadian dentists. PRACTICAL IMPLICATIONS: This validated measure will contribute to understanding of the mental health impact of the pandemic on dentists in Canada and enable the dental regulatory authorities and organizations to intervene to help dentists.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".