Cultural Perceptions of Health in Asian Indian Adults
Bibliographic record
Abstract
OBJECTIVE: To gain an understanding of the cultural perceptions of health among Asian Indian adults in an urban setting. DESIGN: Qualitative semistructured interviews. SETTING: Midwest urban community PARTICIPANTS: Asian Indian adults (n = 20) aged ≥25 years, who self-identified as 100% Asian Indian descent. PHENOMENON OF INTEREST: Individual interviews were conducted by a trained interviewer to assess cultural perceptions of health. ANALYSIS: Transcript analysis was performed by 2 independent coders using verbatim transcripts. Content analysis was used to identify themes using a grounded theory approach. RESULTS: The salient themes that emerged were a cultural definition of health, acculturation, mental health, and health information. Participants believed good health was associated with the ability to perform daily activities, regular exercise, and eating well. There was a lack of awareness of Asian Indian-specific body mass index categories and that overweight and obesity were an important risk factor for chronic diseases. CONCLUSION AND IMPLICATIONS: These data provide a context for health promotion efforts and underscore a gap in awareness of risk factors risk for chronic diseases among the Asian Indian community. Culturally specific interventions targeted at the Asian Indian population, considering their worldview and perceptions of health, will help address this important public health concern.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".