Is there evidence for factorial invariance of the COVID Stress Scales? an analysis of North American and cross-cultural populations
Bibliographic record
Abstract
The COVID-19 pandemic impacted the mental health of more citizens globally than any previous modern viral outbreak. In response to the psychological challenges associated with COVID-19, the COVID Stress Scales (CSS) were developed to assess the presence and severity of COVID-related distress. The initial North American validation study of the CSS identified that the scale comprised five factors: danger and contamination fears, fear of socioeconomic consequences, xenophobia, checking and reassurance seeking, and traumatic stress symptoms. The CSS have since been validated across a multitude of international populations. However, findings support a five- and six-factor model. Methodological issues make interpreting most studies supporting a five-factor model challenging. The purpose of this study was to re-evaluate the factor structure of the CSS using data from North American samples, to assess for potential factorial invariance, and compare these results to cross-cultural findings. Multiple confirmatory factor analyses (mCFA) were conducted across 28 different groups (e.g., age, ethnicity/race, sex) from two large independent North American samples from 2020 (n = 6827) and 2021 (n = 5787), assessing the fit indices of the five-, six-, and alternative-factor model of the CSS. The current results provide evidence for factorial invariance of the six-factor model of the CSS across different North American demographics and highlight potential challenges in interpreting the results of studies that have supported a five-factor model of the CSS.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".