Examining the well-being of military spouses in the context of the COVID-19 pandemic using the Quality of Life survey
Bibliographic record
Abstract
Introduction: Quality of Life (QOL) surveys have been administered to Canadian military spouses for almost two decades, providing valuable information on the health and well-being of Canadian Armed Forces (CAF) families across multiple domains. In 2022, the QOL survey explored the key issues that military spouses faced during the COVID-19 pandemic while also navigating the various demands of military life. Methods: QOL survey respondents were asked a series of questions about the challenges they faced both in relation to the demands of military life and in the context of the COVID-19 pandemic. Regression analyses were conducted to examine associations between these challenges and two indicators of well-being - mental health and thriving - among civilian military spouses who completed the survey (n = 1,016). Results: Consistent with past QOL results, the top issues that continue to challenge civilian military spouses include relocation, primary health care, and spousal employment. Notably, almost half of spouses underlined concerns about well-being and work-life balance, which may reflect the lingering effects of the pandemic. As well, despite evidence pointing toward decreased mental health among CAF spouses in the most recent survey, most respondents were thriving. Results of regression analyses emphasized concerns around health, social connection, and spousal employment as robust correlates of well-being. Discussion: Insights gained from the QOL surveys have enabled the CAF to take a pulse on spousal and family well-being over time, serving a key function in guiding the development of programs and policies that better support the needs of military families.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".