Assessing and optimizing patient-provider communication regarding cardiovascular rehabilitation (VRCOMM)
Bibliographic record
Abstract
"Cardiovascular rehabilitation (CR) is proven to reduce morbidity and mortality in cardiac patients. Despite the evidence of benefit, only 15-20% of patients participate. The most successful strategy to promote CR utilization is systematic referral through healthcare provider (HCP) discussions with the patients. The objectives of this study were to: (1) describe patient-HCP interaction regarding CR at the bedside, and (2) investigate which elements were related to patient referral and enrollment. \n\nThis was a prospective study of cardiovascular patients (n=58) and their HCPs (n=60) who received, a digital audiorecorder to record their subsequent interaction, about "secondary prevention". All HCP and patient participants completed a self-report survey assessing sociodemographic characteristics, perceptions of CR and their clinical interaction. Fifty patient-HCP interactions were successfully digitally recorded and coded using the Roter Interaction Analysis System, a method of coding medical dialogue. \n\nThe results show that, CR referral- making following a cardiovascular event was not allocated to a specific HCP; therefore HCP awareness of patient's referral was incredibly low. Some elements of patient-HCP communication were significantly related to patient referral and enrollment in CR programs weeks later. These elements were: greater HCP interactivity, less patient concern and worry, less HCP reassurance and optimism, and more time allocated to patient questions related to lifestyle. Further tests is needed to examine whether HCPs can be trained to communicate with cardiovascular patients in a manner that enhances CR enrollment rates."
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".