Social and lifestyle predictors of perceived health in the United States: A replication and extension of Statistics Canada.
Bibliographic record
Abstract
Using United States Behavioral Risk Factor Surveillance Survey (BRFSS) and Census data, this study replicated and extended previous research conducted using the Canadian Community Health Survey (CCHS) by Statistics Canada. It examines the associations among both lifestyle and social determinants predictors and a criterion of perceived health. Results were also compared cross-culturally (United States and Canada). The study used secondary data analysis of 2000 and 2001 United States and Census data. In particular, multiple linear regression (MLR) and hierarchical linear modeling (HLM) were used to analyze state and individual-level data. Unlike data at the aggregate level (Canadian health regions and states of the United States), results at the individual-level were consistent across the United States and Canada. Social determinants of health (socioeconomics) were better predictors of health than lifestyle (behaviors). Individual-level socioeconomic characteristics and lifestyle were better predictors than higher level contexts (i.e., characteristics of a state or health regions). The findings of this study suggest that health educators should further research, and increase the focus in teaching and service on, social determinants of health in addition to efforts emphasizing lifestyles (health behaviors). This recommendation aligns with the soon to be released Healthy People 2020 that will add social determinants of health as a priority area for public health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".