Associations between negative COVID-19 experiences and symptoms of anxiety and depression: a study based on a representative Canadian national sample
Bibliographic record
Abstract
INTRODUCTION: Amid the widespread impact of the COVID-19 pandemic, a notable increase in symptoms of anxiety and depression has become a pressing concern. This study examined the prevalence of anxiety and depression symptoms in Canada from September to December 2020, assessing demographic and socioeconomic influences, as well as the potential role of COVID-19 diagnoses and related negative experiences. METHODS: Data were drawn from the Survey on COVID-19 and Mental Health by Statistics Canada, which used a two-stage sample design to gather responses from 14 689 adults across ten provinces and three territorial capitals, excluding less than 2% of the population. Data were collected through self-administered electronic questionnaires or phone interviews. Analytical techniques, such as frequencies, cross-tabulation and logistic regression, were used to assess the prevalence of anxiety and depression symptoms, the demographic characteristics of Canadians with increased anxiety and depression symptoms and the association of these symptoms with COVID-19 diagnoses and negative experiences during the pandemic. RESULTS: The study found that 14.62% (95% CI: 13.72%-15.51%) of respondents exhibited symptoms of depression, while 12.89% (95% CI: 12.04%-13.74%) reported anxiety symptoms. No clear differences in symptom prevalence were observed between those infected by COVID-19, or those close to someone infected, compared to those without these experiences. However, there were strong associations between traditional risk factors for depressive and anxiety symptoms and negative experiences during the pandemic, such as physical health problems, loneliness and personal relationship challenges in the household. CONCLUSION: This study provides insight into the relationship between COVID-19 and Canadians' mental health, demonstrating an increased prevalence of anxiety and depression symptoms associated with COVID-19-related adversities and common prepandemic determinants of these symptoms. The findings suggest that mental health during the pandemic was primarily shaped by traditional determinants of depression and anxiety symptoms and also by negative experiences during the pandemic.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".