Pandemic-related PTSD symptoms and substance use among community-based adults
Bibliographic record
Abstract
Abstract Objectives: To examine: (1) the role of gender and socioeconomic status in pandemic-related post-traumatic stress disorder (PTSD) symptoms and substance use; (2) associations between probable PTSD and substance use, and (3) the supports needed to address these problems. Methods: Data were collected in June 2020 from 933 community-based adults in Alberta without a previous diagnosis of PTSD. 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 probable PTSD and substance use. Results: Significantly more women (19%) than men (13%) met criteria for probable pandemic–related PTSD, while a similar percentage (13.5% of women, 13.0% of men) reported increased substance use during the pandemic. Adults with lower income, education, or pandemic-related job loss were more vulnerable to PTSD and substance use increases. Probable pandemic-related PTSD was associated with increased substance use for both women (OR = 2.2) and men (OR = 2.3) in adjusted models. Many adults (50% of women, 40% of men) indicated they needed support to address mental health or substance use during the pandemic, particularly from friends, a physician, and/or a counsellor. Conclusions: This study examined adults who had just experienced two months of increasing COVID-19 cases and containment measures. Findings suggest women and socioeconomically vulnerable adults may be in greater need of mental health supports, and that pandemic-related PTSD is an important consideration for interventions to reduce substance use among both women and men.
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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) | 0.007 | 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".