A qualitative study of CVD management and dietary changes: problems of ‘too much’ and ‘contradictory’ information
Bibliographic record
Abstract
BACKGROUND: Nutrition education for cardiovascular disease (CVD) management is not effective for all population groups. There is little understanding of the factors that hinder patients from adhering to dietary recommendations. METHODS: 37 interviews were conducted with people living with CVD in Adelaide, Australia. Recruitment occurred via General Practitioner (GP) clinics and hospital cardiac rehabilitation programs. Participants were either receiving preventive treatment or active treatment for established CVD. RESULTS: The volume and contradictory nature of dietary information were the most prominent barriers to making changes identified in interviews, especially by order participants. CONCLUSION: Patients will seek out, or come into contact with information which contradicts advice from their GPs. The volume of information may lead them to resort to old and familiar habits. GPs play a valuable role in highlighting key take-home messages and reliable external sources of information. The findings have implications for GP practice given that lifestyle changes are a cost- and clinically-effective means of managing CVD.
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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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".