Facilitators and Barriers to Healthy Eating in Aged Chinese Canadians with Hypertension: A Qualitative Exploration
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".