Patients’ perceptions of early palliative oncology care: A qualitative analysis.
Bibliographic record
Abstract
e20639 Background: Early palliative care referral is encouraged for patients with advanced cancer. However, little is known about patients’ perceptions of the impact and relevance of early referral. We conducted a qualitative study in which patients with advanced cancer were interviewed following completion of a randomized controlled trial comparing early palliative care with standard oncology care. Our aim was to delineate what, in the opinion of patients, were the respective roles of the oncology and palliative care teams in an outpatient setting. Methods: We conducted qualitative interviews with patients following completion of a cluster randomised controlled trial of early versus routine palliative care referral. Participants were recruited from 24 medical oncology clinics at a comprehensive cancer center. Selective sampling was employed to ensure equivalent numbers of participants from intervention vs. control arms, male vs. female, age ≥60 vs. <60 years, with high vs. low self-reported quality of life, and with high vs. low satisfaction with care. Forty-eight patients (26 intervention and 22 control) with advanced lung, breast, gynecological, gastrointestinal and genitourinary cancers completed interviews lasting 25 to 90 minutes. Control patients were asked about the role of oncology; intervention patients were asked about both teams. Interviews were recorded, transcribed and analysed using NVivo. A grounded theory approach was used to explore emerging themes. Results: Several themes emerged in relation to the contrast between oncology (OC) and palliative care (PC) including (1) the focus of the consultation, with OC tending to focus on cancer or treatment options while PC was regarded as being more holistic, including physical, psychological and family domains; (2) the model of care delivery, with OC described as being clinician-led and time-limited, in contrast with PC where time was more flexible and the patient set the agenda; and (3) the complementary nature of early palliative care alongside standard oncology care in terms of overall well-being. Conclusions: From a patient perspective, palliative care and medical oncology have distinct and complementary roles, supporting the relevance of early referral.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".