Is Anxiety Sensitivity Associated with COVID-19 Related Distress and Adherence among Emerging Adults?
Bibliographic record
Abstract
We investigated whether anxiety sensitivity (AS) is associated with increased distress and adherence to public health guidelines during the COVID-19 pandemic among undergraduates, and whether increased distress mediates the relationship between AS and increased adherence. An online cross-sectional survey was conducted with 1318 first- and second-year undergraduates (mean age of 19.2 years; 79.5% females) from five Canadian universities. Relevant subscales of the Substance Use Risk Profile Scale (SURPS) and the Big Five Inventory-10 (BFI-10) were used to assess AS and neuroticism. Three measures tapped distress: the Patient Health Questionnaire-9 (PHQ-9) for depressive symptoms, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety symptoms, and the Brief COVID-19 Stress Scales (CSS-B) for COVID-19-specific distress. The COVID-19 Adherence scale (CAD) assessed adherence to COVID-19 containment measures. AS was significantly independently associated with higher general distress (both anxiety and depressive symptoms) and higher COVID-19-specific distress, after controlling age, sex, study site, and neuroticism. Moreover, AS indirectly predicted greater adherence to COVID-19 preventive measures through higher COVID-19-specific distress. Interventions targeting higher AS might be helpful for decreasing both general and COVID-19-specific distress, whereas interventions targeting lower AS might be helpful for increasing adherence to public health containment strategies, in undergraduates.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".