Self-reported health behaviour change in adults: analysis of the Canadian Community Health Survey 4.1
Bibliographic record
Abstract
INTRODUCTION: Knowledge of Canadians' experiences in making health behaviour changes (HBCs) in general, and among those at risk due to body mass index (BMI), would help inform health promotion / disease prevention programs. Selected self reported HBCs in the past 12 months by BMI category were examined in this secondary analysis of the Canadian Community Health Survey 4.1. These HBCs included increased sports/exercise, weight loss and improved eating habits. Barriers to HBC were also examined. METHODS: Descriptive analyses and forward stepwise logistic regression were completed on data from respondents 18 years and older. Self-reported BMI was corrected by the method of Connor Gorber et al. (2008). RESULTS: Our final sample was n = 111 449. Overall, 58% of respondents had made an HBC, with increased sports/exercise as the most important HBC in 29% of the sample, followed by improved eating habits (10%) and weight loss (7%). Half (51%) experienced barriers to HBC; lack of will power was most commonly cited, followed by work and family responsibilities. Obese respondents reported HBC more frequently than normal-weight respondents (60% vs. 55%), but the prevalence of increased sports/ exercise and improved eating habits was similar across BMI categories. Regression models accounted for only 6%-10% of the total variance. CONCLUSION: That a majority of respondents had made at least one HBC bodes well for positively shifting population health. Additional work to further characterize the population, and to improve on population indicators, is needed to assess the impact of health promotion/disease prevention efforts. These findings provide important first population benchmarks for future work.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".