The Role of Systematic Inpatient Cardiac Rehabilitation Referral in Increasing Equitable Access and Utilization
Bibliographic record
Abstract
In Brief BACKGROUND: While systematic referral strategies have been shown to significantly increase cardiac rehabilitation (CR) enrollment to approximately 70%, whether utilization rates increase among patient groups who are traditionally underrepresented has yet to be established. This study compared CR utilization based on age, marital status, rurality, socioeconomic indicators, clinical risk, and comorbidities following systematic versus nonsystematic CR referral. METHODS: Coronary artery disease inpatients (N = 2635) from 11 Ontario hospitals, utilizing either systematic (n = 8 wards) or nonsystematic referral strategies (n = 8 wards), completed a survey including sociodemographics and activity status. Clinical data were extracted from charts. At 1 year, 1680 participants completed a mailed survey that assessed CR utilization. The association of patient characteristics and referral strategy on CR utilization was tested using χ2. RESULTS: When compared to nonsystematic referral, systematic strategies resulted in significantly greater CR referral and enrollment among obese (32 vs 27% referred, P = .044; 33 vs 26% enrolled, P = .047) patients of lower socioeconomic status (41 vs 34% referred, P = .026; 42 vs 32% enrolled, P = .005); and lower activity status (63 vs 54% referred, P = .005; 62 vs 51% enrolled, P = .002). There was significantly greater enrollment among those of lower education (P = .04) when systematically referred; however, no significant differences in degree of CR participation based on referral strategy. CONCLUSION: Up to 11% more socioeconomically disadvantaged patients and those with more risk factors utilized CR where systematic processes were in place. They participated in CR to the same high degree as their nonsystematically referred counterparts. These referral strategies should be implemented to promote equitable access. This study compared cardiac rehabilitation (CR) utilization based on demographics, socioeconomic indicators, clinical risk, and comorbidities following systematic versus nonsystematic inpatient CR referral among 2635 coronary artery disease inpatients. Up to 11% more socioeconomically disadvantaged patients and those with more risk factors utilized CR where systematic processes were in place.
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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 | 0.020 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".