The association between risky health behaviours and socio-economic status in Canadian Armed Forces recruits: the mediating effect of health-risk attitudes
Bibliographic record
Abstract
Introduction: Socio-economic status (SES) is significantly associated with health outcomes and behaviours. Low SES in childhood has been found to predict poor health outcomes in young adulthood. Furthermore, low SES has been linked to poor health-risk attitudes. It is important to understand the correlates of risky health behaviours and the nature of the relationships among risky health behaviours, SES, and attitudes favouring risky health behaviours. Methods: The Recruit Health Questionnaire (RHQ) was developed to determine demographic information and health behaviours of Canadian Armed Forces (CAF) recruits and is collected during the early weeks of basic military training. We examined the relationship between SES (as measured by previous year’s household income) and risky health behaviours (e.g., smoking, risky drinking), and explored the mediating effect of attitudes favouring health risks (e.g., likelihood of drinking heavily at a social function). We analyzed data from 3,465 CAF recruits (83% male; 76% non-commissioned member recruits; 43% between the ages of 20 and 24 years; 63% having completed at least some post-secondary studies) who completed the RHQ between 2013 and 2014. Results: Low SES was associated with smoking, and high SES with binge drinking and driving under the influence, but, when controlling for age, sex, rank, and education level, there was a significant indirect effect of SES on all risky health behaviours through health-risk attitudes. This suggests that SES prior to military enlistment may affect health behaviours, but health-risk attitudes play a significant role in contributing to risky behaviours. Discussion: It is important to understand the role of SES background on recruits and its role in determining attitudes toward health and risky health behaviours in order to influence policies and targeted health promotion programming to reduce risky health behaviours. Knowing that health-risk attitudes play a significant role in risky health behaviours can influence programming to promote attitudes that are more averse toward health and safety risks.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".