Randomized Controlled Trial of Tailored Nursing Interventions to Improve Cardiac Rehabilitation Enrollment
Bibliographic record
Abstract
BACKGROUND: Short hospital stays for patients with acute coronary syndromes (ACSs) reduce the opportunity for risk factor intervention during admission. After discharge, cardiac rehabilitation can decrease the recurrence of coronary events by up to 25%. However, it remains underused. OBJECTIVES: The aim of this study was to determine whether a nursing intervention focused on individual ACS patients' perceptions of their disease and treatment would increase rehabilitation enrollment after discharge. METHOD: A total of 242 ACS patients admitted to a specialized tertiary cardiac center were randomized to either the intervention or usual care (n = 121 in both groups). The intervention included one nurse-patient meeting before discharge with 2 additional contacts over the 10 days after discharge (mean duration = 40 minutes per contact). The primary outcome was enrollment in a free rehabilitation program offered to all participants 6 weeks after discharge. Secondary outcomes included illness perceptions; family support; anxiety level; medication adherence; and cardiac risk factors including lack of exercise, smoking, body mass index, and diet. RESULTS: The sample was composed of a majority of male, married workers who experienced a myocardial infarction or unstable angina without severe complications. The mean hospital stay in both groups was 3.6 days. There was a significantly higher rate of rehabilitation enrollment in the intervention group (45%) than in the control group (24%; p = .001). For the secondary outcomes, only the personal control dimension of illness perceptions was improved significantly with the intervention. DISCUSSION: Progressive, individualized interventions by nurses resulted in greater rehabilitation enrollment, thereby potentially improving long-term outcome.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".