Effects of anxiety sensitivity, disgust, and intolerance of uncertainty on the COVID stress syndrome: a longitudinal assessment of transdiagnostic constructs and the behavioural immune system
Bibliographic record
Abstract
Excessive fear and worry in response to the COVID-19 pandemic (e.g., COVID stress syndrome) is prevalent and associated with various adverse outcomes. Research from the current and past pandemics supports the association between transdiagnostic constructs-anxiety sensitivity (AS), disgust, and intolerance of uncertainty (IU)-and pandemic-related distress. Recent research suggests a moderating effect of disgust on the relationship of AS-physical concerns and COVID-19-related distress, suggesting that transdiagnostic constructs underlie individual differences in activation of the behavioral immune system (BIS). No previous study has examined the independent and conjoint effects of pre-COVID-19 AS-physical concerns, disgust propensity (DP), disgust sensitivity (DS), and IU in this context; thus, we did so using longitudinal survey data (N = 3,062 Canadian and American adults) with simple and moderated moderations controlling for gender, mental health diagnosis, and COVID-19 diagnosis. Greater AS-physical concerns, DP, and DS predicted more severe COVID stress syndrome assessed one month later. Either DP or DS further amplified the effect of AS-physical concerns on COVID stress syndrome, except danger and contamination fears. IU did not interact with AS-physical concerns and DS or DP. Theoretical and clinical implications pertaining to delivery of cognitive behavioural therapy for pandemic-related distress are discussed.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 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".