Early palliative intervention: effects on patient care satisfaction in advanced cancer
Bibliographic record
Abstract
Objective In a cluster-randomised controlled trial of early palliative care (EPC) in advanced cancer, EPC was robustly associated with increased patient satisfaction with care. The present study evaluated mediational mechanisms underlying this EPC effect, including improved physical and psychological symptoms and quality of life, as well as relationships with healthcare providers and preparation for end of life. Method Participants with advanced cancer (n=461) completed measures at baseline and then monthly to 4 months. Mediational analyses, using a robust bootstrapping approach, focused on 3-month and 4-month follow-up data. Results At 3 months, EPC decreased psychological symptoms, which resulted in greater satisfaction either directly (βindirect effect=0.05) or through greater quality of life (βindirect effect=0.02). At 4 months, EPC increased satisfaction through improved quality of life (βindirect effect=0.08). Physical symptom management showed no significant mediational effects at either time point. Better relationships with healthcare providers consistently mediated the EPC effect on patient satisfaction at 3 and 4 months, directly (βindirect effect=0.13–0.16) and through reduced psychological symptoms and/or improved quality of life (βindirect effect=0.00–0.02). At 4 months, improved preparation for end-of-life mediated EPC effects on satisfaction by enhancing quality of life (βindirect effect=0.01) or by reducing psychological symptoms and thereby increasing quality of life (βindirect effect=0.02). Conclusion EPC increases satisfaction with care in advanced cancer by attending effectively to patients’ emotional distress and quality of life, enhancing collaborative relationships with healthcare providers, and addressing concerns about preparation for end-of-life. Trial registration number NCT01248624
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".