Latent Class Analysis Offers Insight into the Complex Food Environments of Native American Communities: Findings from the Randomly Selected OPREVENT2 Trial Baseline Sample
Bibliographic record
Abstract
Native Americans (NAs) experience a high burden of obesity and diabetes, yet previous research has not holistically described the unique food environments of NA communities. The objective of this paper is to describe the subgroups and demographic characteristics related to NA household food environments. Surveys collected food getting, food assistance, and sociodemographic variables from randomly selected adults from three NA communities (n = 300) in the Midwest and Southwest. Exploratory latent class analysis (LCA) identified the appropriate number of subgroups based on indicator responses. After assigning participants to classes, demographic differences were examined using bivariate analyses. NA household food environments could be described using two subgroups (“lower” and “higher access household food environments”). The “lower access” group had significantly higher age, smaller household size, and fewer children per household than the “higher access” group, while body mass index (BMI) did not significantly vary. This is the first LCA of NA household food environments and highlights the need for approaches that characterize the complexity of these environments. Findings demonstrate that NA household food environments can be described by developing subgroups based on patterns of market and traditional food getting, and food assistance utilization. Understanding NA household food environments could identify tailored individual and community-level approaches to promoting healthy eating for NA Nations.
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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 | 0.008 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".