Improving Early Palliative Care with a Scalable, Stepped Peer Navigator and Social Work Intervention: A Single-Arm Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Patients with cancer could benefit from early primary (i.e., basic) palliative care. Scalable models of care delivery are needed. OBJECTIVE: Examine the feasibility of a stepped peer navigator and social work intervention developed to improve palliative care outcomes. DESIGN: Single-arm prospective clinical trial. The peer navigator educated patients to advocate for pain and symptom management with their healthcare providers, motivated patients to pursue advance care planning, and discussed the role of hospice. The social worker saw patients with persistent psychosocial distress. SETTING/SUBJECTS: Patients with advanced cancer at a VA Medical Center not currently in palliative care or hospice whose oncologist would not be surprised if the patient died in the subsequent year. MEASUREMENTS: Participation and retention rates, patient-reported symptoms and quality of life, advance directive documentation, patient satisfaction survey, and semistructured interviews. RESULTS: The participation rate was 38% (17/45), and 35% (7/17) completed final survey measures. Patients had stage IV (81%) and primarily genitourinary (47%) and lung (24%) malignancies. Median Eastern Cooperative Oncology Group performance status was 0. Patient-reported surveys indicated low distress (mean scores: Functional Assessment of Cancer Therapy-General, 75.3 [standard deviation {SD} 17.6]; Edmonton Symptom Assessment Scale symptom scores ranged from 1.6 to 3.8; Patient Health Questionnaire-9, 5.7 [SD 5.2]; and Generalized Anxiety Disorder-7, 2.8 [SD 4.1]). Of those who had not completed advance directives at baseline (n = 11, 65%), five completed them by the end of study (5/11, 45%). Patients who completed satisfaction surveys (n = 7) and interviews (n = 4) provided mixed reviews of the intervention. CONCLUSIONS: At a single site, a stepped peer navigator and social work palliative care study had several challenges to feasibility, including low patient-reported distress and loss to follow-up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".