Composite health behaviour classifier as the basis for targeted interventions and global comparisons in men’s health
Bibliographic record
Abstract
INTRODUCTION: Lifestyle-related diseases are the leading cause of death among North American men. We evaluated health behaviours and their predictors that contribute to morbidity and mortality among Canadian men as a means to making recommendations for targeted interventions. METHODS: A cross-sectional analysis of Canadian men drawn from 5362 visitors to our online survey page was conducted. The current study sample of 2000 men (inclusion: male and >18 years; exclusion: incomplete surveys) were stratified to the 2016 Canadian census. The primary outcome was the number of unhealthy men classified using our Canadian Composite Classification of Health Behaviour (CCCHB) score. Secondary outcomes included the number of men with unhealthy exercise, diet, smoking, sleep, and alcohol intake, as well as socioeconomic and demographic factors associated with unhealthy behaviours to be used for targeting future interventions. RESULTS: Only 118/2000 (5.9%) men demonstrated 5/5 healthy behaviours, and 829 (41.5%) had 3/5 unhealthy behaviours; 391 (19.6%) men currently smoked, 773 (38.7%) demonstrated alcohol overuse, 1077 (53.9%) did not get optimal sleep (<7 or >9 hours per night), 977 (48.9%) failed to exercise >150 minutes/week, and 1235 (61.8%) had an unhealthy diet. Multivariate analysis indicated that men with high school education were at increased risk of unhealthy behaviours (odds ratio [OR] 1.58; 95% confidence interval [CI] 1.15-2.18; p=0.005), as were men living with relatives (OR 2.10; 95% CI1.04-4.26; p=0.039), or with their partner and children (OR 1.34; 95% CI 1.02-1.76; p=0.034). CONCLUSIONS: An overwhelming 41.5% of Canadian men had 3/5 unhealthy behaviours, affirming the need for targeted lifestyle interventions. Significant health inequities within vulnerable subgroups of Canadian men were identified and may guide the content and delivery of future interventions.
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".