Determinants of a healthy lifestyle and use of preventive screening in Canada
Bibliographic record
Abstract
BACKGROUND: This study explores the associations between individual characteristics such as income and education with health behaviours and utilization of preventive screening. METHODS: Data from the Canadian National Population Health Survey (NPHS) 1998-9 were used. Independent variables were income, education, age, sex, marital status, body mass index, urban/rural residence and access to a regular physician. Dependent variables included smoking, excessive alcohol use, physical activity, blood pressure checks, mammography in past year and Pap smear in past 3 years. Logistic regression models were developed for each dependent variable. RESULTS: 13,756 persons 20 years of age and older completed the health portion of the NPHS. In general, higher levels of income were associated with healthier behaviours, as were higher levels of education, although there were exceptions to both. The results for age and gender also varied depending on the outcome. The presence of a regular medical doctor was associated with increased rates of all preventive screening and reduced rates of smoking. CONCLUSION: These results expand upon previous data suggesting that socioeconomic disparities in healthy behaviours and health promotion continue to exist despite equal access to medical screening within the Canadian healthcare context. Knowledge, resources and the presence of a regular medical doctor are important factors associated with identified differences.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".