Access to Cardiac Rehabilitation Among South-Asian Patients by Referral Method: A Qualitative Study
Bibliographic record
Abstract
People of South-Asian origin have an increased prevalence of coronary artery disease. Although cardiac rehabilitation (CR) is effective, South Asians are among the least likely people to participate in these programs. Automatic referral increases CR use and may reduce access inequalities. This study qualitatively explored whether CR referral knowledge and access varied among South-Asian patients. Participants were South-Asian cardiac patients receiving treatment at hospitals in Ontario, Canada. Each hospital refers to CR via one offour methods: automatically through paper or electronically, through discussion with allied health professionals (liaison referral), or through referral at the physician's discretion. Data were collected via interviews and analyzed using interpretive-descriptive analysis. Four themes emerged: the importance of predischarge CR discussions with healthcare providers, limited knowledge of CR, ease of the referral process for facilitators of CR attendance, and participants'needs for personal autonomy regarding their decision to attend CR. Liaison referral was perceived to be the most suitable referral method for participants. It facilitated communication between patients and providers, ensuring improved understanding of CR. Automatic referral may not be as well suited to this population because of reduced patient-provider communication.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".