Anxiety in adults with asthma during the coronavirus disease 2019 pandemic: a Canadian perspective
Bibliographic record
Abstract
BACKGROUND: Asthma is a chronic airway inflammatory disease that affects millions of Canadians and often contributes to higher levels of anxiety among patients. Since the coronavirus disease 2019 (COVID-19) pandemic was a time of increased anxiety and fear among the Canadian population, it was thought that those with asthma may experience heightened anxiety levels due to uncertain access to care, the potential to misinterpret asthma symptoms for symptoms of COVID-19 (or vice versa), and the concern about being treated differently by those around them when experiencing asthma symptoms. Therefore, this study sought to perform a cross-sectional analysis of the asthma-anxiety relationship in adults with and without asthma in the unique context of the COVID-19 pandemic from a Canadian perspective. METHODS: This study employed the COVID-19 Associated Anxiety in Allergic Rhinitis and Asthma patients Experiencing Symptoms (CAAARES) survey, consisting of COVID-19-specific questions, the Generalized Anxiety Disorder Assessment-7 (GAD-7) and the Asthma Control Questionnaire-6 (ACQ-6). Data collection occurred through the Qualtrics XM platform and data analyses were conducted with the IBM SPSS Statistics 28 software. RESULTS: A total of 741 valid responses were collected (asthma group, n = 244; control group, n = 497). 31.6% and 26.2% of respondents in the asthma and control groups, respectively, met the diagnostic criteria for GAD. There was no significant difference (p = .067) in mean GAD-7 scores between the two groups. A Hierarchal Multiple Regression (HMR) model was developed, and neither asthma status nor ACQ-6 score had a significant predictive effect on the GAD-7 score. There was a statistically significant (p < .001) weak positive correlation (r = .22) between GAD-7 and ACQ-6 scores. In a simple mediation (SMM) model, perceived COVID-19 stress of others was not identified as a significant mediator of the relationship between ACQ-6 and GAD-7 (indirect effect β = 0.014). CONCLUSION: Our study of a Canadian cohort demonstrates elevated levels of anxiety overall, amongst both asthma and control groups. While AR status was significantly greater in the asthma group, it was not a significant predictive variable of GAD-7 score. Our data suggests that COVID-19-specific factors appear to have a greater contribution to anxiety than asthma status or control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".