Implementing the Serious Illness Care Program in Safety Net Health Systems: A Qualitative Study
Bibliographic record
Abstract
CONTEXT: Interventions to improve the quality of care for people affected by serious illness commonly fail to reach patients from marginalized and underserved communities, which include those characterized by racialized or indigenous identity, sexual and gender minority status, and rural living. Interventions to improve care through serious illness conversations have demonstrated benefit, but little is known about their implementation in health systems that predominantly serve these patient groups. OBJECTIVES: The study aimed to understand factors influencing implementation of a serious illness communication-focused intervention-the Serious Illness Care Program in health systems who primarily provide care to marginalized and underserved communities. METHODS: Qualitative interviews (16) and focus groups (3) were conducted with 19 interdisciplinary team members from six geographically diverse U.S. healthcare systems. Using a template analysis approach, investigators coded data inductively and deductively to identify themes. RESULTS: Three themes emerged: patient factors, intervention elements, and health system contextual factors. Participants highlighted mission-driven efforts, creativity, interprofessional practice, and trainees as enablers of success. They identified weaknesses in the intervention's communication tool-the Serious Illness Conversation Guide as barriers to implementation of conversations. Resource constraints, socio-economic vulnerability, and mistrust in the health system were seen as additional barriers. CONCLUSIONS: Health systems that provide care to underserved and marginalized communities face unique challenges implementing the Serious Illness Care Program. They also possess assets, some unique to these settings, that support program adoption. Findings suggest that implementation of similar programs in low-resource healthcare settings may help address unmet needs among marginalized populations.
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 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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".