Symptoms of major depressive disorder during the COVID-19 pandemic: results from a representative sample of the Canadian population
Bibliographic record
Abstract
INTRODUCTION: Since the outbreak of COVID-19, numerous studies from around the world have reported declines in mental health. However, most of these studies were of low-to-moderate quality and many were based on convenience samples or used mental health measures with low validity, or both. Consequently, it has been difficult to draw conclusions. METHODS: Both the 2020 Survey on COVID-19 and Mental Health (SCMH) and the Canadian Community Health Survey (CCHS) (2015-2019) used the Patient Health Questionnaire-9 to screen for major depressive disorder (MDD) in adults aged 18 or older. The prevalence of MDD was compared between the SCMH and the CCHS. Risk and protective factors for MDD in the SCMH were examined using bivariate and logistic regression analyses. RESULTS: Based on SCMH data, 15.2% (95% CI: 14.2-16.2) of Canadians screened positive for MDD. The prevalence of MDD was more than two times higher in the SCMH (during COVID-19) than in the CCHS (predating COVID-19). In bivariate analysis, Canadians reporting five or more COVID-19-related risk factors were close to 30 times more likely to have MDD than those reporting no risk factors. Mastery and a sense of community belonging were protective factors for MDD. CONCLUSION: After remaining stable for two decades, the prevalence of depression among Canadians increased substantially with the onset of COVID-19. Ongoing monitoring of this common condition associated with major morbidity is vital to determine if elevated levels of MDD persist as we progress through and beyond future waves of COVID-19.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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".