The Psychometric Properties of the French–Canadian Stress and Anxiety to Viral Epidemics-6 Scale for Measuring the Viral Anxiety of the General Population During the COVID-19 Pandemic
Bibliographic record
Abstract
Objective: This study examined the psychometric properties of the French-Canadian version of the Stress and Anxiety to Viral Epidemics-6 items (SAVE-6) scale for assessing the anxiety response to the viral epidemic among the general population in Quebec, Canada. Methods: A total of 590 participants responded to a confidential online survey between September 28 and October 18, 2020. Confirmatory Factor Analysis (CFA) was conducted to explore the factor structure of the scale. Psychometric properties were assessed using the Item Response Theory (IRT) approach. To explore the convergent validity, a Pearson correlation analysis between the SAVE-6 scale and the depression (Patient Health Questionnaire-2, PHQ-2) or anxiety subscale (Generalized Anxiety Disorder-2, GAD-2) of the Patient Health Questionnaire-4 items scale was conducted. Findings: = 0.048), and the multi-group CFA revealed that the SAVE-6 scale can measure anxiety response in the same way across gender or the presence of elevated depressive and anxiety symptoms. It showed good internal consistency (Cronbach's alpha = 0.76, McDonald's Omega = 0.77) and significant correlation with the PHQ-2 score and GAD-2 score. The IRT model suggested the efficiency in discrimination among individuals in this latent trait. Conclusion: The French-Canadian version of the SAVE-6 scale is a valid and reliable rating scale, which can measure the general population's anxiety response to the viral epidemic.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".