139 Practices, facilitators and barriers for specialist palliative care referral among doctors in the department of cardiology of Khoo Teck Puat hospital in Singapore
Bibliographic record
Abstract
Background Heart failure (HF) is a chronic, progressive disease with increasing prevalence, including up to 4.5% in Singapore. Despite advancements in treatment, the prognosis remains poor, with 1-year mortality rates ranging from 25% to 75%. The 2020 guidelines from the Singapore Heart Failure Society emphasize the importance of early palliative care integration in HF management. However, compared to cancer patients, HF patients are less frequently referred to Specialist Palliative Care (SPC). This study aims to explore the referral practices of doctors within the Department of Cardiology, the timing and frequency of SPC referrals, and the barriers to referring HF patients to palliative care. Methods A cross-sectional, prospective qualitative survey was conducted among cardiology consultants, resident/staff physicians, and medical officers at Khoo Teck Puat Hospital. A questionnaire adapted from studies in Canada and Germany was used, covering demographics, training, SPC referral practices, and perceived barriers. Data were analyzed using SPSS Version 27.0, with Chi-square tests applied to compare responses between senior and junior doctors, and a p-value of <0.05 considered statistically significant. Results The survey revealed that while palliative care was recognized as beneficial for HF patients, referrals to SPC were often delayed, typically occurring in the advanced stages of the disease. Key barriers included discomfort with discussing end-of-life care and uncertainty about the appropriate timing for SPC referral. Senior doctors reported greater discomfort in these discussions compared to junior doctors. Conclusions The study highlights the need for earlier and more frequent referrals to SPC for HF patients. Addressing the identified barriers, including clarifying referral guidelines, could improve the integration of palliative care into HF management and ultimately improve patient outcomes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".