COVID-19 and the Mental Health of Canadian Armed Forces Veterans: A Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: There are no data on the impact of COVID-19 and associated public health measures, including sheltering at home, travel restrictions, and changes in health care provision, on the mental health of older veterans. This information is necessary for government and philanthropic agencies to tailor mental health supports, services, and resources for veterans in the peri- and post-pandemic periods. The objective of this study was to compare mental health symptoms between Canadian Armed Forces (CAFs) veterans and the general Canadian older adult population in the early months of the COVID-19 pandemic. MATERIALS AND METHODS: This was a secondary analysis of a cross-sectional study of older adults in the national Canadian COVID-19 Coping Study. Individuals aged 55 years and older were eligible. A convenience sample of older adults was recruited through a web-based survey administered between May 01, 2020 and June 30, 2020. Canadian Armed Force military service history status (yes/no) was ascertained. The eight-item Center for Epidemiological Studies Depression Scale, the five-item Beck Anxiety Inventory, and the three-item Loneliness Scale were used to measure mental health symptoms. Multivariable logistic regression compared the odds of screening positive for depression, anxiety, and loneliness between veterans and non-veterans. RESULTS: Of 1,541 respondents who answered the final question (87% survey completeness rate), 210 were veterans. Forty percent of veterans met criteria for at least one of the mental health diagnoses compared to 46% of non-veterans (P = .12). The odds of reporting elevated symptoms of depression, anxiety, and loneliness were similar for veteran and non-veteran respondents after adjusting for confounders. CONCLUSION: Veterans' report of mental health symptoms was similar to the general population Spring 2020 of the COVID-19 pandemic. Although veterans' military training may better prepare them to adapt in the face of a pandemic, additional research is needed to understand the longitudinal impacts on physical and mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".