Patients’ and caregivers’ views on the timing and benefits of early palliative care: A qualitative study.
Bibliographic record
Abstract
157 Background: Early palliative care referral can improve quality of life and satisfaction with care, and is increasingly encouraged. However, little is known about patients’ and caregivers’ attitudes towards early referral. We conducted qualitative interviews seeking the opinions of advanced cancer patients (who had been randomized to an early palliative care intervention) and their caregivers, to determine whether they perceived a benefit, and if so in which domains. Methods: We recruited participants from medical oncology clinics at a comprehensive cancer centre, following completion of a randomized controlled trial comparing early palliative care referral with standard oncology care. Selective sampling was employed to ensure equivalent numbers of participants based on study arm, age, gender, high vs. low quality of life scores, and high vs. low satisfaction with care. A grounded theory approach was used to explore emerging themes. Results: Twenty-six patients and 14 caregivers completed interviews. Several benefits of early referral were noted. These included: prompt attention to symptom needs; timely, sensitive information about prognosis and end-of-life care options; and destigmatization of palliative care through routine referral. Although some participants did not feel they currently needed palliative care, they did feel comforted that early referral had provided them with a “safety net” that could quickly be put into place if they required future support. Conclusions: Early referral was perceived as useful in several domains, including immediate symptom control, and preparation for the future.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".