Cardiac Rehabilitation Wait Times
Bibliographic record
Abstract
In Brief PURPOSE: Cardiac rehabilitation (CR) is a proven effective means for secondary prevention of coronary heart disease. Timely access to CR services is key to promoting patient participation and ensuring optimal patient outcomes. Despite wait time benchmarks having been established, research regarding how long patients wait to enter CR following referral receipt is limited. The aim of this study was to (a) describe wait times from CR referral to intake assessment and (b) examine the association of wait time to CR enrollment rates. METHODS: Wait time from date of CR referral to date of intake assessment was calculated in days for 599 participants referred to CR from 2006 to 2009 inclusive. A descriptive examination of sociodemographic and clinical characteristics was performed, followed by logistic regression analysis to assess the wait time by enrollment relationship. RESULTS: Median wait time from referral receipt to CR intake was 42.0 days. Wait time had a negative effect on CR enrollment, such that for every 1-day increment in wait time, patients were 1% less likely to enroll. CONCLUSIONS: The time that patients wait to enroll in CR may affect the number of patients who choose to attend, and longer wait times may mean fewer patients will benefit from CR participation. Programs should be encouraged to undertake quality improvement initiatives to ensure wait times are not negatively impacting patient enrollment and ultimately preventing patients from benefiting from CR participation. Further research is needed to establish evidence-based wait time benchmarks and interventions to promote timely access to CR services. The aim of this study was to develop a self-reported version of the Chronic Heart Questionnaire (CHQ-SR). The CHQ-SR was found to be comparable with the interview-led CHQ and was repeatable, responsive, and had construct validity. The CHQ-SR requires neither interviewer time nor associated cost, providing for a practical administration of the questionnaire.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".