Impact of social determinants of health on obesity among American Indian and Alaska Native young adults
Bibliographic record
Abstract
We examined the prevalence of obesity among American Indian and Alaska Native (AIAN) young adults and to investigate the association between key social determinants of health (SDOH) and higher body mass index (BMI). We used the Indian Health Service Improving Delivery Data Project from fiscal year 2013. It includes data for 20,698 AIAN young adults aged 18-24 years. We added county-level measures of SDOH from the USDA Food Environment Atlas and the Census as contextual variables. We conducted stratified logistic regressions to understand the relationship between these SDOH indicators and odds of obesity. Thirty-seven percent of our sample was identified as obese (i.e., BMI ≥30). Individuals who lived in counties with lower levels of educational attainment and higher levels of poverty had higher odds of obesity than those who lived in counties with higher education and lower poverty (p < 0.0001). Counties with higher poverty rates had less access to social and environmental resources than the lower poverty rate counties (p < 0.0001). Federal and state governments should increase access to education and economic development opportunities to positively impact health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".