Clustering of health behaviours in Canadians: A multiple behaviour analysis of data from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Health risk behaviours such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease and each play a central role in limiting health and life satisfaction. However, much less is known about how co-occurring behaviours are associated with health outcomes. Understanding which behaviours tend to co-occur (i.e., cluster together), and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide novel opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behaviour change. Using cross-sectional baseline data (N=40,268) from the Canadian Longitudinal Study of Aging, we performed a pre-defined set of analyses to examine the co-occurrence of health behaviours. We used agglomerative hierarchical cluster analysis to cluster individuals based on their behavioural tendencies and multinomial logistic regression to examine how these clusters are associated with demographic characteristics, healthcare utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. Seven clusters were identified with clusters differentiated by six of the seven health behaviours included in the analysis. Variability between clusters was observed in frequencies of weekly walking, strenuous exercise, and alcohol consumption. Sociodemographic characteristics varied across several clusters while self-reported physical/mental health showed less variation across clusters. The seven identified clusters of health behaviours allow for contrasts to be made with comparable analyses in other countries and will help inform the development of future health behaviour change interventions tailored to sub-populations and their sociodemographic profiles.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".