Socioeconomic status, diabetes, and gestation length in Native American and White women.
Bibliographic record
Abstract
OBJECTIVE: "Diminishing returns" of socioeconomic status (SES) suggests that higher SES may not confer equivalent health benefits for ethnic minority individuals as compared to White individuals. Little research has tested whether diminishing returns also affects Native Americans. The objective of this study was to determine whether higher SES is associated with lower diabetes risk and longer gestational length in both Native American and White women, and whether SES predicts gestational length indirectly via diabetes risk. METHOD: A sample of 674,014 Native American and White women was drawn from a population-based California cohort of singleton births (2007-2012). Education, public health insurance status, gestational length, and diabetes diagnosis were extracted from a state-maintained birth cohort database. Covariates were age, health behaviors, pregnancy variables, residence rurality, and prepregnancy body mass index. RESULTS: In logistic regression models, the race by SES interaction (both education and insurance status) was associated with diabetes risk. Compared to high-SES White women, high- and low-SES Native American women had highest and equivalent diabetes risk. In path analyses, the race by SES interaction indirectly predicted gestational length through diabetes, ps < .001. For White women, an indirect effect of diabetes was detected, ps < .001, such that higher SES was associated with reduced risk for diabetes and thus longer gestational length. For Native American women, no indirect effect was detected, ps > .067. CONCLUSIONS: Among Native American women, higher SES did not confer protection against diabetes or shorter gestational length. These findings are consistent with the diminishing returns of SES phenomenon. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.000 | 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".