Financial hardship, positive financial behaviour, and behavioural health outcomes among U.S. soldiers
Bibliographic record
Abstract
Introduction: Financial well-being is an ongoing concern among military personnel, and more research is needed to understand the implications of both financial hardship and positive financial behaviours on behavioural health and well-being. Methods: Using a sample of active duty U.S. Army personnel (N = 850), the authors estimated the prevalence of self-reported past-year financial hardships and positive financial behaviors and examined the relationship of these factors with behavioural health. Results: At least one financial hardship was reported by 31% of participants, whereas 74% engaged in at least one positive financial behaviour. Junior enlisted personnel and personnel with lower income, less education, and larger families reported more financial hardship and fewer positive behaviours. Adjusted multivariate logistic regression analyses indicated that financial hardship was associated with posttraumatic stress disorder, depression, anxiety, fewer sleep hours, insomnia, and self-rated health, but not alcohol misuse. Following a budget was associated with lower alcohol misuse, but there were no other significant relationships between positive financial behaviors and the outcomes. Discussion: This study demonstrates that financial hardship is a risk factor for behavioural health problems among military personnel. Findings illustrate the need to evaluate existing military financial literacy and support programs and regulations intended to improve financial well-being or prevent poor financial decision-making. Future research should contemplate fine-tuned measurement strategies and the role of individual differences in these relationships of interest.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".