How does COVID stress vary across the anxiety-related disorders? Assessing factorial invariance and changes in COVID Stress Scale scores during the pandemic
Bibliographic record
Abstract
BACKGROUND: No studies have examined whether levels of COVID stress vary across anxiety-related disorders. Likewise, no studies have assessed structural invariance of the COVID Stress Scales (CSS) across clinical diagnoses. We sought to address these issues in the present study. Given the dynamic nature of pandemics, we also assessed whether COVID stress changed from the first to third wave in those with clinical diagnoses and those with no mental health conditions. METHOD: Data were collected during COVID-19 from two independent samples of adults assessed about a year apart (early-mid in 2020, N = 6854; and early-mid 2021, N = 5812) recruited from Canada and the United States through an online survey. Participants provided demographic information, indicated the presence of current (i.e., past-year) anxiety-related or mood disorder, and completed the CSS. RESULTS: The five CSS were reliable (internally consistent), and the five-factor structure was stable across samples. Scores tended to be highest in people with anxiety-related or mood disorders, particularly panic disorder. As expected, scores fluctuated over time, being higher during the early phases of the pandemic when threat was greatest and lower during the later phases, when vaccines were deployed and the COVID-19 threat was reduced. CONCLUSION: The findings add to the growing number of studies supporting the psychometric properties of the CSS. The results encourage further investigations into the utility of the scales, such as their ability to detect treatment-related changes in COVID-19-related distress. The scales also show promise for studies of future pandemics or outbreaks because the CSS can be modified, with minor wording changes, to assess distress associated with all kinds of disease outbreaks.
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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 | 0.006 | 0.019 |
| 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 | 0.001 |
| Open science | 0.001 | 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".