The prevalence of mental health and addiction concerns and factors associated with depression and anxiety during the COVID-19 pandemic in Ontario, Canada: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Higher than expected rates of mental health and/or addiction (MHA) concerns have been documented since the onset of the COVID-19 pandemic. A more up-to-date prevalence of MHA outcomes and the factors associated with the occurrence of MHA concerns remains unclear. This study examined the prevalence of MHA outcomes and factors associated with screening positive for symptoms of depression only, anxiety only, and both depression and anxiety two years into the COVID-19 pandemic in Ontario, Canada. METHOD: Ontario adults ≥18 years of age (n = 5000) reported on the presence of symptoms associated with depression, anxiety, and substance use between January and March 2022. Data were also collected on pandemic-related health variables, including COVID-19 infection fear, changes in socioeconomic status and mental health since pandemic onset, satisfaction with social supports, and MHA service needs. RESULTS: The prevalence of positive screening for depressive or anxiety symptoms only was 8% and 11%, respectively, while 36% screened positive for both. Moderate/high risk levels of substance use were found in 20% of participants for tobacco and 17% for both alcohol and cannabis. Moderate/high risk levels of alcohol use and certain pandemic-related factors (negative change in mental health, unmet MHA service needs) were associated with positive screening for symptoms of depression only, anxiety only, and both depression and anxiety. Satisfaction with social supports was associated with lower likelihoods of being in the depression only and both depression and anxiety groups, and non-White ethnicity was associated with depression only. CONCLUSIONS: There was a continued burden of MHA issues two years into the pandemic. These results underscore the ongoing need for timely and accessible MHA services.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".