Referral Practices of Cardiologists to Specialist Palliative Care in Canada
Bibliographic record
Abstract
Background Patients with heart failure have palliative care needs that can be effectively addressed by specialist palliative care (SPC). Despite this, SPC utilization by this patient population is low, suggesting barriers to SPC referral. We sought to determine the referral practices of cardiologists to SPC. Methods Cardiologists across Canada were invited to participate in a survey about their referral practices to SPC. Associations between referral practices and demographic, professional, and attitudinal factors were analyzed using multiple and logistic regression. Results The response rate was 51% (551 of 1082). Between 35.1% and 64.2% of respondents were unaware of referral criteria to local SPC services. Of the respondents, 29% delayed SPC referral because of prognostic uncertainty, and 46.8% believed that SPC prioritizes patients with cancer. In actual practice, nearly three-fourths of cardiologists referred late. Referral frequency was associated with greater availability of SPC services for patients with nonmalignant diseases ( P = 0.008), a higher number of palliative care settings accepting patients receiving continuous infusions or pursuing acute care management ( P < 0.001), satisfaction with services ( P < 0.001), and less equation of palliative care with end-of-life care ( P < 0.001). Early timing of referral was associated with greater availability of SPC services for patients with nonmalignant diseases and less equation of palliative care with end-of-life care. Conclusions The findings suggest that barriers to timely SPC referral include an insufficiency of services for patients with nonmalignant diseases especially in the outpatient setting, the perception that SPC services do not accept patients receiving cardiology-specific treatments, and a misperception about the identity of palliative care.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".