Assessing the impact of COVID-19 on self-reported levels of depression during the pandemic relative to pre-pandemic among Canadian adults
Bibliographic record
Abstract
OBJECTIVES: This study aims to assess the impact of COVID-19 related risk factors on self-reported increases in depression among Canadian adults during the pandemic compared to pre-pandemic levels. We aim to investigate the interactive effects of stressors, including social isolation, financial stress, and fear of catching COVID-19, on mental health outcomes. Our study aims to provide insights for the development of prevention and intervention strategies to address the mental health effects of the pandemic by examining the psychological changes attributable to the pandemic and its impact. METHODS: This study used data collected from the Mental Health Research Canada online survey during the third wave of COVID-19 (April 20-28, 2021). The study examined the impact of COVID-19 related factors, including social isolation, financial concerns, fear of catching COVID-19, and concerns about paying bills, on self-reported increases in depression. Multivariable logistic regression models were utilized to examine these associations, with adjustments made for potential confounding variables. All statistical analysis was performed using SAS V9.4 (SAS Institute Inc., Cary, NC, USA). RESULTS: Participants reporting social isolation, financial concerns, and fear of catching COVID-19 were more likely to report increased depression. An interaction was observed between concerns for paying bills and catching COVID-19 in relation to depression (p = 0.0085). In other words, the effect of concerns about paying bills on depression was stronger for individuals who also had a fear of catching COVID-19, and vice versa. Young adults, females, patients with pre-existing depression, and residents of certain provinces reported higher levels of depression during COVID-19. CONCLUSION: Our study underscores the significant impact of the COVID-19 pandemic on mental health, particularly among certain demographic groups. It emphasizes the need for depression screening and increased support for mental health during the pandemic, with a focus on mitigating financial burdens and reducing negative psychological impacts of social isolation. Our findings highlight the complex interplay between different stressors and the need to consider this when designing interventions to support mental health during times of crisis.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".