Well-Being of Canadian Veterans during the COVID-19 Pandemic: Cross-Sectional Results from the COVID-19 Veteran Well-Being Study
Bibliographic record
Abstract
Abstract Background The impacts of the COVID-19 pandemic have disproportionally affected different population groups. Veterans are more likely to have pre-existing mental health conditions compared to the general Canadian population, experience compounded stressors resulting from disruptions to familial, social, and occupational domains, and were faced with changes in healthcare delivery (e.g., telehealth). The objectives of this study are to assess (a) the mental health impact of COVID-19 and related life changes on the mental health of Veterans and (b) perceptions of and satisfaction with changes in healthcare treatments and delivery during the pandemic. Methods A total of 1139 Canadian Veterans were recruited to participate in an online survey. Participants completed questions pertaining to their mental health and well-being, lifestyle changes, and concerns relating to the COVID-19 pandemic, as well as experiences and satisfaction with healthcare treatments during the pandemic. Results Results showed that 55.9% of respondents reported worse mental health functioning compared to before the pandemic. Frequency of probable posttraumatic stress disorder, major depressive disorder, generalized anxiety disorder, alcohol use disorder, and suicidal ideation were 34.2%, 35.3%, 26.8%, 13.0%, and 22.0%, respectively. Between 39.0% and 53.0% of respondents attributed their symptoms as either directly related to or exacerbated by the pandemic. Approximately 18% of respondents reported using telehealth for mental health services during the pandemic, and among those, 73.0% indicated a choice to use telehealth even after the pandemic. Conclusions This study found that most Veterans experienced worsening mental health as a result of the COVID-19 pandemic. The use of telehealth services was widely endorsed by mental health treatment-seeking Veterans who transitioned to virtual care during the pandemic. Our findings have important clinical and program administrator implications, emphasizing the need to reach out to support veterans, especially those with pre-existing mental health conditions and to enhance and maintain virtual care even post pandemic.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".