Well-being of Canadian Armed Forces Veterans and Spouses of Veterans During the COVID-19 Pandemic: Protocol for a Prospective Longitudinal Survey
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has resulted in significant changes to everyday life, including social distancing mandates, changes to health care, and a heightened risk of infection. Previous research has shown that Canadian Armed Forces (CAF) veterans are at higher risk of developing mental and physical health conditions. Veterans and their families may face unique social challenges that can compound with pandemic-related disruptions to negatively impact well-being. OBJECTIVE: This study aims to longitudinally characterize the mental health of CAF veterans and spouses of CAF veterans throughout the pandemic and to understand the dynamic influences of pandemic-related stressors on psychological health over time. METHODS: We employed a prospective longitudinal panel design using an online data collection platform. Study participation was open to all CAF veterans and spouses of CAF veterans residing in Canada. Participants were asked to complete a comprehensive battery of assessments representing psychological well-being, chronic pain, health care access patterns, physical environment, employment, social integration, and adjustment to pandemic-related lifestyle changes. Follow-up assessments were conducted every 3 months over an 18-month period. This study was approved by the Western University Health Sciences and Lawson Health Research Institute Research Ethics Boards. RESULTS: Baseline data were collected between July 2020 and February 2021. There were 3 population segments that participated in the study: 1047 veterans, 366 spouses of veterans, and 125 veterans who are also spouses of veterans completed baseline data collection. As of November 2021, data collection is ongoing, with participants completing the 9- or 12-month follow-up surveys depending on their date of self-enrollment. Data collection across all time points will be complete in September 2022. CONCLUSIONS: This longitudinal survey is unique in its comprehensive assessment of domains relevant to veterans and spouses of veterans during the COVID-19 pandemic, ranging from occupational, demographic, social, mental, and physical domains, to perceptions and experiences with health care treatments and access. The results of this study will be used to inform policy for veteran and veteran family support, and to best prepare for similar emergencies should they occur in the future. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34984.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".