Opportunities in stroke care at safety net hospitals: A socioecological perspective
Bibliographic record
Abstract
Safety net hospitals (SNHs) provide care to patients regardless of their insurance status or ability to pay, serving populations at the highest risk for poor stroke outcomes. These include historically marginalized racial and ethnic groups, and individuals disproportionately affected by adverse social drivers of health, including lower socioeconomic status, housing instability, and limited access to preventive care. Improving stroke care at SNHs presents a critical opportunity to strengthen care delivery for individuals with the greatest need. However, such efforts require a clear understanding of the barriers across all levels of the healthcare system. In this Perspective, the authors adopt a socioecological framework to explore patient-, community-, institution-, and policy-level influences on stroke care in SNHs. Patient-level barriers include chronic disease burden, limited health literacy, and language barriers. Community-level challenges, such as neighborhood disadvantage, transportation challenges, and food insecurity, contribute to delays in care and recovery. At the institutional level SNHs often face inconsistent access to diagnostic imaging, limited specialty support, variation in stroke center certification, and staffing shortages. At the policy level, financing structures, documentation requirements, and performance metrics may unintentionally penalize under-resourced hospitals. Findings have been synthesized in this text across these domains and highlight opportunities for research, workforce development, and stroke care delivery improvement. A socioecological approach is essential to addressing disparities in stroke outcomes and guiding multilevel strategies that ensure consistent, high-quality care for underserved populations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".