Prevalence of posttraumatic stress disorder (PTSD) in Canada during the COVID-19 pandemic: results from the Survey on COVID-19 and Mental Health
Bibliographic record
Abstract
INTRODUCTION: This study provides a descriptive overview of the prevalence of posttraumatic stress disorder (PTSD) in Canada, across sociodemographic characteristics, mental health-related variables and negative impacts of the COVID-19 pandemic. METHODS: Data were obtained from cycles 1 and 2 of the Survey on COVID-19 and Mental Health (SCMH), collected in fall 2020 (N = 14 689) and spring 2021 (N = 8032). The prevalence of PTSD was measured using the PTSD Checklist for DSM-5 (PCL-5) Cross-sectional associations were quantified using logistic regression, while controlling for sociodemographic characteristics. RESULTS: The overall prevalence of PTSD was 6.9%. Factors associated with higher PTSD prevalence were female gender; younger age; lower income (females only); living in an urban area; frontline worker status or not being at work in the past week (males only); fair or poor mental health; a weak sense of community belonging; symptoms of generalized anxiety disorder and major depressive disorder; suicidal ideation; heavy alcohol use; daily cannabis use; increased alcohol and cannabis use since the start of the pandemic; decreased alcohol consumption since the start of the pandemic (males only); concerns about violence in the home; and negative impacts of the pandemic. CONCLUSION: PTSD prevalence in Canada varies significantly across sociodemographic groups and is more common among those with indicators of lower mental health and well-being, as well as those more adversely affected by the COVID-19 pandemic. Ongoing and enhanced surveillance of PTSD in Canada is important to better understand and address the burden and impacts of this condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".