Acceptability of a Serious Illness Conversation Guide to Black Americans: Results from a focus group and oncology pilot study
Bibliographic record
Abstract
OBJECTIVES: Serious illness conversations (SICs) can improve the experience and well-being of patients with advanced cancer. A structured Serious Illness Conversation Guide (SICG) has been shown to improve oncology patient outcomes but was developed and tested in a predominantly White population. To help address disparities in advanced cancer care, we aimed to assess the acceptability of the SICG among African Americans with advanced cancer and their clinicians. METHODS: A two-phase study conducted in Charleston, SC, included focus groups to gather perspectives on the SICG in Black Americans and a single-arm pilot study of a revised SICG with surveys and qualitative exit interviews to evaluate patient and clinician perspectives. We used descriptive analysis of survey results and thematic analysis of qualitative data. RESULTS: = 23) reported that SICG-guided conversations were acceptable, helpful, and promoted conversations with loved ones. Oncologists found conversations feasible to implement and skill-building, and also identified opportunities for training and implementation that could support meeting the needs of their patients with low health literacy. An adapted SICG includes language to assess the strength and affirm the clinician-patient relationship. SIGNIFICANCE OF RESULTS: An adapted structured communication tool to facilitate SIC, the SICG, appears acceptable to Black Americans with advanced cancer and seems feasible for use by oncology clinicians working with this population. Further testing in other marginalized populations may address disparities in advanced cancer 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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".