Exploring which anxiety-related disorder symptoms and mechanisms are associated with COVID-19 anxiety
Bibliographic record
Abstract
In the wake of the COVID-19 pandemic, a rise in anxiety has been reported among the population. This rise coincides with the introduction of COVID-19 anxiety, which is the fear and emotional distress caused by the COVID-19 pandemic. Previous research has found an association between COVID-19 anxiety and symptoms of health anxiety, panic disorder, and obsessive-compulsive disorder. COVID-19 anxiety has also been associated with mechanisms such as anxiety sensitivity, maladaptive metacognitions, intolerance of uncertainty, and the emotion of disgust. In the current study, self-report questionnaires were used to examine which anxiety-related disorder symptoms, and related mechanisms, were associated with COVID-19 anxiety. A total of 593 MacEwan students completed the study between September 2020 and February 2021. A set of regression analyses examined which anxiety-related disorder symptoms were uniquely associated with COVID-19 anxiety. The two symptoms most associated with COVID-19 anxiety were health anxiety and obsessive-compulsive disorder symptoms. When examining the anxiety-related mechanisms, a second set of regression analyses identified disgust sensitivity and health anxiety-specific intolerance of uncertainty as having the strongest association with COVID-19 anxiety. Based on these findings, clinicians may wish to screen for COVID-19 anxiety in clients experiencing health anxiety, obsessive-compulsive, or panic disorder symptoms. Lastly, clinicians may find it helpful to target the clients' responses to feelings of disgust, and their health anxiety-specific intolerance of uncertainty, when working with clients experiencing high levels of COVID-19 anxiety.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".