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Record W2908287034 · doi:10.3390/nu11010111

Facilitators and Barriers to Healthy Eating in Aged Chinese Canadians with Hypertension: A Qualitative Exploration

2019· article· en· W2908287034 on OpenAlexaffabout
Ping Zou

Bibliographic record

VenueNutrients · 2019
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsNipissing University
Fundersnot available
KeywordsMedicineTelephone interviewIntervention (counseling)Family medicineImmigrationGerontologyQualitative researchNursingPsychology

Abstract

fetched live from OpenAlex

Objectives: To determine the facilitators and barriers influencing healthy eating behaviours among aged Chinese-Canadians with hypertension. Methods: After attending five weeks of dietary educational training (Dietary Approach to Stop Hypertension with Sodium (Na) Reduction for Chinese Canadians; DASHNa-CC), 30 aged Chinese-Canadian participants partook in a telephone interview. Participants were asked to name three facilitators and three barriers that influenced their ability to follow the DASHNa-CC intervention. Telephone transcripts were then analyzed and coded using computer software and categorized into personal, familial, community, and societal facilitators or barriers. Results: Personal factors included health problems, motivation, effects of healthy diet, health-related careers, and dietary habits. Family factors included family structure, support from family members, and critical health events involving family members or relatives. Community factors consisted of educational materials, friends, primary care physicians, and online social networks. Societal factors included accessibility to grocery stores and restaurants. Conclusions: Aged Chinese-Canadian immigrants experience unique facilitators of and barriers to healthy eating, which may warrant further attention from healthcare professionals when educating patients in a culturally-sensitive manner.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.444

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 head0.023
GPT teacher head0.304
Teacher spread0.281 · 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 designObservational
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

Citations33
Published2019
Admission routes2
Has abstractyes

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