Original Research: Can a Palliative Care Lay Health Advisor–Nurse Partnership Improve Health Equity for Latinos with Cancer?
Bibliographic record
Abstract
PURPOSE: A palliative care infrastructure is lacking for Latinos with life-threatening illness, especially in rural regions of the United States. The purpose of this study was to develop and evaluate a community-based palliative care lay health advisor (LHA) intervention for rural-dwelling Latino adults with cancer. METHODS: An exploratory mixed-methods participatory action research design was carried out by an interprofessional research team that included community and academic members. Fifteen Latino community leaders completed a 10-hour palliative care training program and then served as palliative care LHAs. Although 45 Latinos with cancer initially agreed to participate, four withdrew or died and six were not reachable by the LHAs, for a final total of 35 patient participants.The trained palliative care LHAs delivered information on home symptom management and advance care planning to assigned participants. Palliative care nurses led the training and were available to the LHAs for consultation throughout the study. The LHAs made an average of three telephone calls to each participant. The Edmonton Symptom Assessment System-Revised (ESAS-r) and the four-item Advance Care Planning Engagement Survey (ACPES-4) were administered pre- and postintervention to determine the intervention's effectiveness. Encounter forms were transcribed, coded, and analyzed using case comparison. RESULTS: The major finding was that significant improvements were shown for all four items of the ACPES-4 among both the LHAs (posttraining) and the participants (postintervention). Information on advance care planning was shared with 74.3% of the 35 participants. Participants showed clinical improvement in physical symptom scores and clinical deterioration in emotional symptom scores following the intervention, although these changes did not reach statistical significance. The advisors noted that participants were anxious about how to explain cancer to children, the uncertainty of their prognosis, and medical expenses. This sample was younger than those of other cancer studies; 51.4% were under age 50 and 73.1% had at least one child in the home. CONCLUSIONS: A community-based palliative care LHA-nurse partnership was shown to be a feasible way to engage in conversations and deliver information about advance care planning to rural-dwelling Latino adults with cancer. The positive results led to the regional cancer center's decision to select "cultural care" as its 2022 goal for maintaining its accreditation with the Commission on 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".