The impact of patient-healthcare provider discussions on enrollment in cardiovascular rehabilitation
Bibliographic record
Abstract
OBJECTIVE: Secondary prevention programs such as cardiovascular rehabilitation significantly decrease the burden of cardiovascular disease, yet are under-used. The most successful strategy to promote cardiovascular rehabilitation utilization is systematic referral with a patient-provider discussion. This study investigated: (i) the elements of patient-provider discussions related to patient cardiovascular rehabilitation enrollment, and (ii) the frequency and correlates of these discussion elements. DESIGN/PARTICIPANTS: This was a prospective study of cardiovascular patients and their healthcare providers. Discussions about "secondary prevention" were audio-recorded. Utterances were coded using the Roter Interaction Analysis System. Two months later, cardiovascular rehabilitation enrollment was ascertained. RESULTS: Discussions between 26 healthcare providers and 50 patients were recorded, of whom 27 (54.0%) enrolled in cardiovascular rehabilitation. Participants were significantly more likely to enroll in cardiovascular rehabilitation when their healthcare providers offered less reassurance and optimism (odds ratio (OR) = 0.81), and when the patient asked more questions related to lifestyle (OR = 4.98). These were not common. CONCLUSION: While caution is warranted due to the number of comparisons undertaken such that associations observed may be chance associations, these novel findings suggest that not overstating the beneficial effects of acute treatment, and allowing patients more time to ask questions about needed lifestyle changes should be investigated in future research.
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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 | 0.009 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".