Well-being of Canadian Armed Forces members during the COVID-19 pandemic: the influence of positive health behaviours
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has been linked to increased depression, anxiety and other adverse mental health outcomes. Understanding the behaviours that positively influence health is important for the development of strategies to maintain andimprove well-being during the pandemic. METHODS: This study focussed on Canadian Armed Forces Regular Force members (N = 13 668) who participated in the COVID-19 Defence Team Survey, administered between April and May 2020. The use of positive health behaviours and the extent to which such behaviours were associated with anxiety, depression and self-reported change in health and stress levels compared to before the pandemic were examined. RESULTS: Depression and anxiety were experienced by 14% and 15% of the sample, respectively, while 36% reported that their mental health had gotten worse since the pandemic started, and close to half reported worse physical health and stress levels. The most common behaviours respondents reported engaging in to maintain or improve their health were exercising outdoors, healthy eating and connecting with loved ones. Although most behaviours were associated with better health outcomes, meditation and connecting with loved ones showed associations with worse health. CONCLUSION: Engaging in behaviours such as exercise and healthy eating was generally associated with better health outcomes. Unexpected relationships of meditation and connecting with loved ones are discussed in terms of their use in stressful times among those with mental health issues, past research on coping strategies and impacts of the pandemic and physical distancing on social connections. The findings may have implications for strategies to promote healthy behaviours during the remainder of the pandemic and similar crises in the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".