Financial stress, financial stability, and military spousal well-being
Bibliographic record
Abstract
Introduction: Not only do military families face similar financial stressors as civilian families, but they also face a number of unique financial stressors as a result of the military lifestyle. In this article, we explore factors that contribute to military spouses’ perceived financial stress and the effect of financial stability on military spouses’ psychological well-being and life satisfaction. Methods: The data sets used in the analyses include female civilian spouses of Canadian Armed Forces (CAF) Regular Force members who completed the Quality of Life (QOL) survey administered in 2009 ( N = 1,518) and 2013 ( N = 1,189). In Study 1, we conducted a series of analyses to assess whether CAF spouses’ socio-demographic and military lifestyle characteristics contribute to their perceived level of financial stress (2009 QOL data). In Study 2, we conducted a series of analyses to assess whether financial stress and stability contribute to CAF spouses’ well-being and life satisfaction (2009 and 2013 QOL data). Results: Results showed that socio-demographic and military lifestyle characteristics predicted military spouses’ financial stress and that more financial stress and less financial stability reduced their psychological well-being and life satisfaction. Discussion: These findings suggest that reducing military spouses’ financial stress and improving their financial stability may enhance their well-being.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".