Pulmonary referrals to specialist palliative medicine: a survey
Bibliographic record
Abstract
OBJECTIVES: Patients with chronic respiratory disease have significant palliative care needs, but low utilisation of specialist palliative care (SPC) services. Decreased access to SPC results in unmet palliative care needs among this patient population. We sought to determine the referral practices to SPC among respirologists in Canada. METHODS: Respirologists 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 analysed using regression analyses. RESULTS: The response rate was 64.7% (438/677). Fifty-nine per cent of respondents believed that their patients have negative perceptions of palliative care and 39% were more likely to refer to SPC earlier if it was renamed supportive care. While only 2.7% never referred to SPC, referral was late in 52.6% of referring physicians. Lower frequency of referral was associated with equating palliative care to end-of-life care (p<0.001), male sex of respirologist (p=0.019), not knowing referral criteria of SPC services (p=0.015) and agreement that SPC services prioritise patients with cancer (p=0.025); higher referral frequency was associated with satisfaction with SPC services (p=0.001). Late referral was associated with equating palliative care to end-of-life care (p<0.001) and agreement that SPC services prioritise patients with cancer (p=0.013). CONCLUSIONS: Possible barriers to respirologists' timely SPC referral include misperceptions about palliative care, lack of awareness of referral criteria and the belief that SPC services prioritise patients with cancer. Future studies should confirm these barriers and evaluate the effectiveness of strategies to overcome them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".