Barriers and Facilitators to Implementing Primary Stroke Center Policy in the United States: Results From 4 Case Study States
Bibliographic record
Abstract
OBJECTIVES: We identified barriers and facilitators to the state-level implementation of primary stroke center (PSC) policies, which encourage the certification or designation of specialized stroke treatment facilities and may address concerns such as transportation bypass, telemedicine, and treatment protocols. METHODS: We studied the experiences of 4 states (Florida, Massachusetts, New Mexico, and New York) selected from the 18 states that had enacted PSC policies or were actively considering doing so. We conducted semistructured interviews during fieldwork in each case study state. RESULTS: Our results showed that system fragmentation, gaps in human and financial resources, and complexity at the interorganizational and operational levels are common barriers and that policy champions, stakeholder support and communication, and operational adaptation are essential facilitators in the adoption and implementation of PSC policies. CONCLUSIONS: The identification of barriers and facilitators reveals the contextual elements that can help or hinder policy implementation and may be useful in informing policy formulation and implementation in other jurisdictions. Proactively identifying jurisdictional challenges and opportunities may help facilitate the policy process for PSC designation and allow jurisdictions to develop more effective stroke systems of 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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".