Descriptive regression tree analysis of intersecting predictors of adult self-rated health: Does gender matter? A cross-sectional study of Canadian adults
Bibliographic record
Abstract
BACKGROUND: While self-rated health (SRH) is a well-validated indicator, its alignment with objective health is inconsistent, particularly among women and older adults. This may reflect group-based differences in characteristics considered when rating health. Using a combination of SRH and satisfaction with health (SH) could capture lived realities for all, thus enabling a more accurate search for predictors of subjective health. With the combined measure of SRH and SH as the outcome we explore a range of characteristics that predict high SRH/SH compared with predictors of a low rating for either SRH or SH. METHODS: Data were from the Canadian General Social Survey 2016 which includes participants 15 years of age and older. We performed classification and regression tree (CRT) analyses to identify the best combination of socioeconomic, behavioural, and mental health predictors of good SRH and health satisfaction. RESULTS: Almost 85% of the population rated their health as good; however, 19% of those had low SH. Conversely, about 20% of those reporting poor SRH were, none-the-less, satisfied. CRT identified healthy eating, absence of a psychological disability, no work disability from long-term illness, and high resilience as the main predictors of good SRH/SH. Living with a spouse or children, higher social class and healthy behaviours also aligned with high scores in both self-perceived health measures. Sex was not a predictor. CONCLUSIONS: Combining SRH and SH eliminated sex as a predictor of subjective health, and identified characteristics, particularly resilience, that align with high health and well-being and that are malleable.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".