Patient and caregiver satisfaction with ambulatory oncology care: Agreement and predictors.
Bibliographic record
Abstract
e16567 Background: Little is known about the level of agreement between patient and caregiver individual reports of satisfaction with healthcare, and no such study has been conducted in the advanced cancer setting. Our aims were to assess the level of agreement in satisfaction with oncology care, between patients with advanced cancer and their caregivers, and to identify factors associated with satisfaction. Methods: Patients with advanced cancer and their caregivers were recruited from ambulatory oncology clinics. Satisfaction with care was measured using FAMCARE- patient and caregiver versions. Patients completed measures of symptom severity (ESAS) and quality of life (FACT-G, FACIT-Sp). Caregivers completed a quality of life measure (CQOL-C). Agreement between patient and caregiver satisfaction scores was assessed using ICC and weighted kappa statistics; multivariable regression was used to determine individual predictors of satisfaction. Results: For the 191 patient-caregiver pairs, caregivers were less satisfied than patients (mean FAMCARE score 67.5 vs. 65.7 out of a possible 90, p=0.02). Overall agreement between patients and caregivers was moderate (ICC = 0.59). Agreement for individual items on FAMCARE was low (kappa range = 0.11-0.37). Both patients and caregivers were least satisfied with information on managing pain and information regarding prognosis. Factors associated with patient satisfaction were lower education level, better quality of life, and less symptom severity; caregiver satisfaction was associated with lower education levels, and better quality of life. Conclusions: Despite considerable discrepancy between caregiver and patient ratings, there was agreement regarding which elements of care needed most improvement. Greater attention to pain control and communication regarding prognosis would further improve patient and caregiver satisfaction with care in advanced cancer
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".