Factors influencing dietary behavior among elderly patients with hypertension in Wenzhou, China: A qualitative descriptive study
Bibliographic record
Abstract
More than half of the elderly in China have high blood pressure. Dietary management is essential for maintaining ideal blood pressure levels and reducing the risk of hypertension-related complications. However, dietary management of elderly patients with hypertension in China is insufficient, and factors affecting their dietary behaviors are unclear. To explore the factors that may affect dietary behavior among Chinese elderly patients with hypertension. This was a descriptive qualitative study. Fifteen older adults were recruited from a hospital and community in Wenzhou, China, from January 2020 to December 2020. Data were collected through semi-structured interviews, and content analysis was used for data analysis. Three main themes emerged from the data: personal cognition and perception, availability of dietary advice, and social environment. Factors influencing dietary behavior were identified at the individual, information received, and social levels. Personal cognition is the basis for formation of dietary behavior, and applicability of dietary advice and social environment affect the continuity of dietary behavior. Diverse factors affected dietary behavior among Chinese elderly patients with hypertension. Diet management programs should assess patients’ cognition and perception, availability of dietary advice, and social environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".