An Exploratory Analysis of the Correlates of Risk-Taking Propensity in Canadian Military Personnel
Bibliographic record
Abstract
The present paper, accepted on February 14 th, is the final corrected version. There has been growing interest in the impacts of combat exposure on behavioral health outcomes such as alcohol use, risky driving and smoking in research on military personnel in recent years. One psychological factor that may explain such outcomes is an individuals’ risk-taking propensity. The present study thus examined the relationships of risk-taking propensity with demographic variables, deployment history, as well as a number of health and risk behaviors. Data collected as part of a comprehensive health survey in the Canadian Armed Forces (CAF) in 2008 and 2009 were analyzed. Participants included a sample of 2157 Regular Force members, stratified to reflect the Regular Force in terms of rank, sex, and deployment history. Using subscales of the Domain-Specific Risk Taking Scale (DOSPERT), participants’ levels of risk-taking propensity in the health and safety and in the recreational domains were assessed. Results consistently pointed to the higher levels of risk-taking propensity among younger respondents and men. While non-commissioned members (NCMs) reported higher levels of health and safety risk-taking propensity than officers, officers reported higher levels of recreational risk-taking propensity than NCMs. Variation in health and safety, but not recreational risk-taking propensity was found by deployment history. Health and safety risk-taking propensity was associated with a number of health-compromising behaviors (e.g., poor eating habits, inconsistent helmet use, smoking, problem drinking), while recreational risk-taking propensity was associated with a number of health-enhancing behaviors (e.g., good eating habits, physical activity, never smoking). Results thus point to noteworthy variations in the correlates of risk-taking propensity by risk domain among military personnel.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| 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".