Can Community Health Workers Increase Palliative Care Use for African American Patients? A Pilot Study
Bibliographic record
Abstract
PURPOSE: African American patients with cancer underutilize advance care planning (ACP) and palliative care (PC). This feasibility study investigated whether community health workers (CHWs) could improve ACP and PC utilization for African American patients with advanced cancer. METHODS: African American patients diagnosed with an advanced solid organ cancer (stage IV or stage III disease with a palliative performance score < 60%) were enrolled. Patients completed baseline surveys that assessed symptom burden and distress at baseline and 3 months post-CHW intervention. The CHW intervention consisted of a comprehensive assessment of multiple PC domains and social determinants of health. CHWs provided tailored support and education on the basis of iterative assessment of patient needs. Intervention feasibility was determined by patient and caregiver retention rate above 50% at 3 months. RESULTS: Over a 12-month period, 24 patients were screened, of which 21 were deemed eligible. Twelve patients participated in the study. Patient retention was high at 3 months (75%) and 6 months (66%). Following the CHW intervention, symptom assessment as measured by Edmonton Symptom Assessment System improved from 33.8 at baseline to 18.8 ( P = .03). Psychological distress improved from 5.5 to 4.7 ( P = .36), and depressive symptoms from 42.2 to 33.6 ( P = .09), although this was not significant. ACP documentation improved from 25% at baseline to 75% at study completion. Sixty-seven percentage of patients were referred to PC, with 100% of three decedents using hospice. CONCLUSION: Utilization of CHWs to address PC domains and social determinants of health is feasible. Although study enrollment was identified as a potential barrier, most recruited patients were retained on study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".