Association of cultural affinity and island food consumption in the Pacific Islander health study
Bibliographic record
Abstract
Objectives: The dietary patterns of Pacific Islander Americans are partially influenced by a rich cultural heritage. There is little known about how cultural affinity affects the dietary choices of this small, but quickly growing population. This analysis attempts to understand how the association of cultural affinity on island foods consumption (IFC) varies by key demographic characteristics.Design: A sample of 240 Samoan and Tongan adults in California from the Pacific Islander Health Study (PIHS) was used. Psychometric properties of a novel 11-item cultural affinity scale were assessed. Univariate and bivariate analyses of the cultural affinity scale were completed to understand the distribution of cultural affinity score. Separate multivariable Poisson regression was used to assess the effect of interactions between cultural affinity and five key demographic factors on IFC.Results: Psychometric analysis of the PIHS cultural affinity scale revealed two unique factors. Significant interactions were found between cultural affinity and ethnicity and birthplace: the association between cultural affinity and IFC was larger among Samoans compared to Tongans and Samoan or Tongan birthplace was found to have a weaker association between cultural affinity and IFC. Interactions between cultural affinity and age, financial insecurity, and educational attainment were not significant.Conclusion: Understanding how cultural affinity varies in its effect on IFC is a part of understanding overall dietary patterns in this 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".