Psychological distress during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
BACKGROUND: During major pandemics such as COVID-19, the fear of being infected, uncertain prognoses, and the imposition of restrictions may result in greater odds of emotional and psychological distress. Hence, the present study examines the predictors of psychological distress during the COVID-19 pandemic in Canada, and how they differ by gender. METHODS: Data of 2,756 adults aged 18 years and above from a cross-sectional online survey conducted between July and October 2020 was used for this study. A multivariable logistic regression analysis was carried out. The results were presented as adjusted odds ratio (aOR) with their respective confidence interval (CI). RESULTS: Lower odds of psychological distress were found among males compared to females and among individuals aged 45-64 or 65-84 years compared to those aged 18-44. The odds of psychological distress decreased with a rise in income, with individuals whose annual income was greater than or equal to $100,000 being less likely to experience psychological distress compared to those whose income was less than $20,000. The odds of psychological distress were higher among residents of Ontario compared to residents of Quebec. Similarly, the odds of psychological distress were higher among individuals who reported experiencing COVID-19 symptoms compared to those who did not report any COVID-19 symptoms. The disaggregated results by gender showed that age, province, and self-reported COVID-19 symptoms had significant associations with psychological distress in both males and females, but these effects were more pronounced among females compared to males. In addition, income was negatively associated with psychological distress for both males and females, with this effect being stronger among males. CONCLUSION: Five exposure variables (gender, age, province, experiencing COVID-19 symptoms, and total annual income in 2019) significantly predicted the likelihood of reporting psychological distress during the COVID-19 pandemic in Canada. Clearly, there is an imminent need to provide mental health support services to vulnerable groups. Additionally, interventions and policies aimed at combating psychological distress during pandemics such as COVID-19 should be gender specific.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".