Adult PTSD symptoms and substance use during Wave 1 of the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: This study examined associations between pandemic-related PTSD symptoms and substance use among adults, the role of gender and socioeconomic status in these outcomes, and the supports that adults needed to address these problems during Wave 1 of the COVID-19 pandemic in Alberta, Canada. METHODS AND MEASURES: Data were collected from 933 community-based adults without a previous diagnosis of PTSD in June 2020. The Primary Care PTSD Screen was adapted to assess pandemic-related PTSD symptoms. Participants were asked if alcohol or cannabis use had increased in the past month. Adjusted logistic regression models examined associations between pandemic-related PTSD symptoms and substance use. RESULTS: More women (19%) than men (13%) met criteria for high pandemic-related PTSD symptomology, while a similar percentage (13.4% of women, 13.2% of men) reported significant increases in substance use during the pandemic. Adults 18-35 years; those who believed they would become infected with the virus; and those with low income, education, or pandemic-related job loss were more likely to report PTSD symptoms. High pandemic-related PTSD symptomology was associated with a significant substance use increase among both women (OR = 2.2) and men (OR = 2.3) in adjusted models. Many adults (50% of women, 40% of men) reported they needed help to address these problems. CONCLUSIONS: Pandemic-related PTSD symptoms were common among adults during Wave 1 of COVID-19. These symptoms were associated with a significant increase in substance use among women and men. Many adults voiced a need for help with these problems. Findings suggest substance use interventions that consider and address pandemic-related PTSD symptoms may be 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".