Home-Based Palliative Care Services for Underserved Populations
Bibliographic record
Abstract
BACKGROUND: Kokua Kalihi Valley is one of the first federally qualified community health centers offering home-based palliative care (HBPC). Kokua Kalihi Valley serves low-income, immigrant populations from Asia and the Pacific Islands, whose end-of-life needs are rarely addressed. Our team includes a palliative medicine physician, nurse, case manager, psychologist, interpreter, and volunteers. OBJECTIVES: The purpose of this Institutional Review Board-approved study was to measure symptom relief and quality of life, resource utilization, and satisfaction with HBPC. METHODS: Over 12 months, 91 people including 46 patients with chronic advanced illnesses and 45 corresponding primary caregivers were enrolled. Data were collected prospectively, upon admission, and repeatedly thereafter, using the Missoula-Vitas Quality of Life Index, the Edmonton Symptom Assessment Scale, and the Palliative Performance Scale. Utilization of resources was tracked, including case management, hospice, emergency department, and hospital visits. RESULTS: The median age was 71 years, and more than half had chronic neurodegenerative conditions. Most patients (98%) were minority, including Samoans, Filipinos, Japanese, Micronesians, and Hawaiians. Median stay in HBPC was 7 months, with a median of 3.5 visits. Approximately 25% of patients enrolled in hospice (median stay 67.5 days). There was a decrease in hospitalizations (p = 0.002) after HBPC admission. Discussions and documentation of end-of-life wishes increased from 50% to 90% (p < 0.01). Caregiver satisfaction with HBPC was high. CONCLUSION: Data on outcomes and quality indicators of HBPC programs are scant, especially among immigrant Asian and Pacific Islanders patients. Our experience demonstrates the effectiveness of palliative care approaches in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".