Effect of referral strategies on access to cardiac rehabilitation among women
Bibliographic record
Abstract
BACKGROUND: Despite its proven benefits and need, women's access to cardiac rehabilitation (CR) is suboptimal. Referral strategies, such as systematic referral, have been advocated to improve access to CR. This study examined sex differences in CR referral and enrollment by referral strategies; and the impact of referral strategies for referral and enrollment concordance among women. DESIGN: Prospective cohort study. METHODS: This prospective study included 2635 coronary artery disease inpatients from 11 Ontario hospitals that utilized one of four referral strategies. Participants completed a sociodemographic survey, and clinical data were extracted from charts. One year later, 1809 participants (452 (25%) women) completed a mailed survey that assessed CR utilization. Referral strategies were compared among women using generalized estimating equations to control for the effect of hospital. RESULTS: Overall, significantly more men than women were referred (67.2% and 57.8% respectively, p < 0.001), and enrolled in CR (58.6% and 49.3% respectively, p = 0.001). Of the retained women, combined systematic and liaison-facilitated referral resulted in significantly greater CR referral (OR 10.3, 95% CI 4.11-25.58) and enrollment (OR 6.6, 95% CI 4.34-9.92) among women when compared with usual referral. Conversely, concordance between referral and enrollment was greatest following usual referral (K = 0.85), and decreased with referral intensity. CONCLUSIONS: While a lower proportion of referred patients enroll, systematic and liaison-facilitated inpatient referral strategies result in the greatest CR enrollment rates among women. Such strategies have the potential to improve access among women, and reduce 'cherry picking' of patients for referral.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".