Socio-demographic differences in financial strain and satisfaction in the Canadian Armed Forces
Bibliographic record
Abstract
Introduction: This study examines socio-demographic differences in financial strain and financial satisfaction. The life-cycle model of consumption and savings suggests that individuals make less money when they have the most financial responsibilities. It was expected that socio-demographic factors would predict financial strain and satisfaction. Methods: Results from a stratified random sample ( N = 1, 887) of Regular Force members in the Canadian Armed Forces who completed the Military Member/Family Finance Survey (2017) were examined to test whether age, marital status, dependents, sex, rank, and education predicted financial strain and financial satisfaction, both independently and when controlling for family income, using step-wise regression. Results: Members reported higher financial strain and lower financial satisfaction if they had dependents and lower rank; this held when controlling for family income. Separated individuals reported lower financial satisfaction, and women reported higher financial satisfaction than men, although these differences were not significant when controlling for other socio-demographic variables. Discussion: This study replicates US military findings: lower-ranking individuals experience the most financial strain and lowest financial satisfaction, even when controlling for income, age, marital status, and dependents. When women and men were paid equally, women reported higher financial satisfaction, although this was not significant when controlling for other variables. To understand financial strain, militaries and other organizations should focus on those with less power in the organization and those at more vulnerable points in their life cycle (e.g., those who have dependents). Organizations can provide financial counselling to members, ensuring it is targeted to members’ life stage.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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".