Predicting Canadian Armed Forces service couples’ marital satisfaction: roles of financial well-being and financial strain
Bibliographic record
Abstract
Introduction: Civilian-related research has demonstrated that couples’ financial well-being and subtypes of financial strain influence marital satisfaction. However, there is a lesser understanding about financial well-being and strain as well as martial satisfaction among military service couples. Accordingly, the present study examined the impact of service couples’ perception of financial well-being and subtypes of financial strain on their marital satisfaction. Methods: Out of 1,637 Regular Force (Reg F) Canadian Armed Forces members who responded to a financial well-being survey, 423 were married Reg F service couples. Participants reported their financial strains (i.e., credit card debt, relationship problems, physical stress, inability to meet obligations), financial well-being, and marital satisfaction. Results: A hierarchical multiple regression was conducted in which marital satisfaction was regressed onto financial well-being, followed by the subtypes of financial strain. Results indicated that higher financial well-being and lower financial strains both significantly predicted greater marital satisfaction. Fewer relationship problems and higher physical strain were uniquely predictive of higher marital satisfaction. Discussion: Findings suggest that, similar to civilian populations, both financial well-being and financial strains influence service couples’ marital satisfaction. Increased relationship problems with others in relation to finances may decrease the quality of spousal interactions. Meanwhile, it is possible that increased physical strain symptoms (e.g., headaches, inability to sleep, upset stomach) due to financial strain may increase couples’ marital satisfaction through the provision of social support.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".