Current Nutrition Practice in Cardiac Rehabilitation Programs
Bibliographic record
Abstract
PURPOSE: This study sought to determine current practice regarding nutrition care within cardiac rehabilitation (CR) programs, including perceived barriers and facilitators to providing nutrition care in this setting. METHODS: A cross-sectional survey was conducted in October and November 2019. Potential participants were program coordinators, identified through the Australian Cardiovascular Health and Rehabilitation Association program directory and invited to participate via e-mail. RESULTS: Forty-nine respondents (response rate: 13%) are included in this analysis. Programs provided group (n = 42, 86%) and/or individual (n = 25, 51%) nutrition education, and most were supported by a dietitian (63%). However, the availability of dietitians and nutrition care provided at CR was variable. For example, individual education was consistently provided at 13 programs and usually by health professionals other than dietitians. Eight programs (16%) used a formal behavior change framework for nutrition care. Generally, respondents were positive about the role of nutrition; CR coordinators perceived nutrition as a valuable component of the program, and that they had good nutrition knowledge. An identified barrier was the financial resources available to support the provision of nutrition care. CONCLUSIONS: To ensure that patients receive the benefits of evidence-based nutrition care, program staff may require additional support, particularly regarding the use of evidence-based behavior change techniques. Key facilitators that may be leveraged to achieve this include the high value and priority that CR program coordinators place on nutrition care.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".