Co-occurrence of depression, anxiety and increased alcohol use during the late stage of the COVID-19 pandemic in Saskatchewan, Canada: a cross-sectional survey
Bibliographic record
Abstract
Background: Impact of the COVID-19 pandemic on mental health and substance use is well recognised. COVID-19 impacted Saskatchewan particularly hard as it has a higher prevalence of alcohol consumption than the national average. Our study investigated the prevalence and associated factors of co-occurrence of poor mental health and alcohol consumption (also referred to as dual experience) among Saskatchewan adults. Method: Cross-sectional data of 1034 eligible adults collected between July and November 2022, were analysed. Dual experience was defined as mild to severe symptoms of depression (Patient Health Questionaire-9) and/or anxiety (Generalised Anxiety Disorder-7) AND increased alcohol consumption during the later stage of COVID-19 pandemic. Multivariable binary logistic regression models were fitted to identify the factors that are associated with dual experience. Results: The prevalence of different forms of dual experience was 7.32% for depression and alcohol use, 6.09% for anxiety and alcohol use and 5.44% for co-occurrence of depression, anxiety and alcohol use. Dual experiences were less likely among participants from racialised groups, and more likely among those with household food insecurity, as well as concerns over alcohol consumption. Conclusion: Our analysis suggests that Saskatchewan adults are still experiencing poor mental health due to the impact of the COVID-19 pandemic, and a large proportion of people continue to consume alcohol at a higher rate than before the pandemic. Data driven interventions, for example, improving mental health and substance use treatment and counselling services, harm reduction strategies, especially targeting people living in food insecure households, are needed.
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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 | 0.003 |
| Science and technology studies | 0.002 | 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".