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Record W4401843880 · doi:10.1016/j.jneb.2024.07.005

Cultural Perceptions of Health in Asian Indian Adults

2024· article· en· W4401843880 on OpenAlexvenueno aff
Susmita Sadana, Colleen Spees, Bhuvaneswari Ramaswamy, Christopher A. Taylor

Bibliographic record

VenueJournal of Nutrition Education and Behavior · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsAsian IndianPerceptionPsychologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.158

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.415
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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