Apoyo con Cariño (support with caring): RCT protocol to improve palliative care outcomes for Latinos with advanced medical illness
Bibliographic record
Abstract
Latinos are more likely to experience uncontrolled pain, and institutional death, and are less likely to engage in advance care planning. Efforts to increase access to palliative care must maximize primary palliative care and community based models to meet the ever-growing need in a culturally sensitive and congruent manner. Patient navigator interventions are community-based, culturally tailored models of care that have been successfully implemented to improve disease prevention, early diagnosis, and treatment. We have developed a patient navigation intervention to improve palliative care outcomes for seriously ill Latinos. We describe the protocol for a National Institute of Nursing Research-funded randomized controlled trial designed to determine the effectiveness of the manualized patient navigator intervention. We aim to enroll 240 Latino adults with non-cancer, advanced medical illness from both urban and rural clinical sites. Participants will be randomized to the intervention group (five palliative care patient navigator visits plus bilingual educational materials) or control group (usual care plus bilingual educational materials). Outcomes include quality of life (Functional Assessment of Chronic Illness Therapy), advance care planning (Advance Care Planning Engagement survey), pain (Brief Pain Inventory), symptom management (Edmonton Symptom Assessment Scale-revised), hospice utilization, and cost and utilization of healthcare resources. This culturally tailored, evidence-based, theory-driven, innovative patient navigation intervention has significant potential to improve palliative care for Latinos, and facilitate health equity in palliative and end-of-life care.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.007 |
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".