Contextual Associations of Interregional Income Gap with Physical Constitution and Dietary Environment in Individual Housebound Elderly
Bibliographic record
Abstract
BACKGROUND: Much attention has been directed towards the issue of health inequalities associated with Japan’s widening income gap. Focusing on the housebound elderly, we assessed the contextual associations of the interregional income gap with body mass index (BMI). METHOD: A total of 15,200 housebound elderly living in 46 of the country’s 47 prefectures, except for Tokyo, were interviewed face-to-face using a questionnaire that covered age, gender, height, weight, medical history, utilization of nursing care, family, source of income, food consumption, and physical activity. To determine the relationship between BMI and the above-mentioned items, a linear regression analysis was performed. In the multilevel analysis, we assumed a prefecture-level random intercept on the basis of the data on the average income per capita in the 46 prefectures. RESULTS: Valid responses without missing data were obtained from 10,226 respondents (response rate: 67.3%) and used for the analyses; females accounted for 78.5% (n=8,027) of the sample. In the multilevel analysis, prefectural average income showed a significant contextual negative association with BMI in females (-0.846; P=0.001). CONCLUSIONS: Prefectural average income has a significant negative contextual association with individual-level BMI for females; females with a low rate of going out have lower BMI; and females living with children have higher BMI. Social environment may be correlated with BMI in the older population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".