Southern California Asian Americans and the Obesity Epidemic: A Qualitative Study to Improve Understanding and Cultural Competence
Bibliographic record
Abstract
Background Obesity continues to be a public health concern in the United States, yet limited research exists on the impact of obesity and weight gain among Asian Americans.Purpose The purpose of this study was to illuminate the lived realities and physical, mental, and social impacts of obesity on overweight and obese Asian Americans in Southern California.Methods A basic qualitative exploratory approach was used. Twenty-five participants were interviewed, leveraging a protocol derived from the health belief model (HBM).Results Six themes highlight obesity’s impact among Asian Americans in Southern California: physical and mental health disadvantages, motivation and consistency, taboo nature of being overweight or obese, model minority misnomer, cultural incongruence, and perception of BMI as an outdated measure.Discussion Findings reveal obesity’s impact among Asian Americans to be complex, linked to both internal and external factors, and suggest that this population is as important as others when discussing obesity prevention.Translation to Health Education Practice Health Educators and health professions need to improve their understanding of the complexities and impact obesity has among Asian Americans and increase cultural competence to develop more responsive obesity and weight interventions and Health Education content.A AJHE Self-Study quiz is online for this article via the SHAPE America Online Institute (SAOI) http://portal.shapeamerica.org/trn-Webinars
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".