Concurrent Experience of Self-Reported Mental Health Symptoms and Problematic Substance Use During the First Two Years of the COVID-19 Pandemic Among Canadian Adults: Evidence from a Repeated Nationwide Cross-Sectional Survey
Bibliographic record
Abstract
This study was aimed at identifying the prevalence of concurrent experience, poor mental health and problematic substance use, and its associated factors, among Canadian adults during the COVID-19 pandemic. A nationwide repeated cross-sectional sample of 14,897 Canadian adults (quota-sampled, weighted) were recruited on ten occasions between October 2020 and March 2022 using online panels. Concurrent experience was defined as mild to severe symptoms of depression (Patient Health Questionnaire-9) and/or anxiety (Generalized Anxiety Disorder-7) AND meeting screening criteria for problematic cannabis (Cannabis Use Disorder Identification Test-Revised) and/or problematic alcohol use (Alcohol Use Disorder Identification Test). Multivariable binary logistic regression models were fitted to identify the associated factors of concurrent experience using Stata v14.2 SE software. The pooled prevalence of concurrent experience was 17.12%, and 45.54% of the participants reported at least one experience (mental health symptoms or problematic substance use). The highest prevalence of concurrent experience per province was reported in Saskatchewan (19.4%) and the lowest in Quebec (13.6%). Younger adults, male respondents, those identifying as 2SLGBTQ+, self-reporting ethnocultural minority status, diagnostic history of mental health and substance use disorder, suicidal ideation, and lower ability to handle unexpected/difficult situations were significantly associated with concurrent experience during the COVID-19 pandemic in Canada. This analysis showed that the COVID-19 pandemic significantly impacted mental health and substance use in interrelated ways. Data-driven province-specific interventions might be helpful toward a client-centered and integrated mental health and substance use care system in Canada.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".