The impact of deployment length on the investing, saving, and spending behaviours of Canadian Armed Forces members
Bibliographic record
Abstract
Introduction: Very little research has examined the impact of the length of deployments on financial behaviours. Canadian Armed Forces (CAF) members receive different levels and types of compensation for deployments that are more or less than 60 days, both of which can affect members’ saving and spending behaviours. Thus, the present study examines whether the financial behaviours of CAF members who have been deployed differs from those who have not and whether deployment length affects members’ financial behaviours. Methods: Among 1,887 Regular Force CAF members completing a financial survey, participants who had been deployed in the previous two years ( n = 356) were asked to indicate whether they had increased their spending and whether they had saved or invested more money as a result of their deployment. Results: CAF members who had been deployed were less likely than members who had not been deployed to engage in negative financial behaviours and were more likely to report positive financial behaviours. Those who had been deployed for longer than 60 days indicated that they had increased the amount of money saved or invested as a result of their deployment. Spending did not change significantly as a result of deployment. Discussion: This study provides a preliminary examination of the financial behaviours of deployed CAF members. Findings suggest that the length of deployment impacts members’ saving habits. CAF financial planners may use these findings to help tailor their services to the needs of personnel returning from varying lengths of deployments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".