Abstract TP49: State of the Florida Stroke Coordinator: Hospital Inventory Survey Insights
Bibliographic record
Abstract
Background: Stroke coordinators (SC’s) are essential leaders of certified stroke centers which facilitate evidence-based stroke care, improving patient outcomes. Although this role has existed since the 1970’s, significant variability of responsibilities and staffing exists. Evidence based recommendations for SC staffing are scarce. Purpose: Utilizing the 2023 Florida Stroke Registry Hospital Inventory Survey (HIS), we describe current SC roles, responsibilities, and challenges in Florida. Methods: The Florida Stroke Registry (FSR), with state funding, tracks and measures Florida’s stroke center performance. FSR recently deployed the FSR HIS, a ten-part questionnaire examining various aspects of stroke program infrastructure. The survey was disseminated to 171 sites with 38 responses in the first wave. This is preliminary data from an HIS section focusing on SC staffing, workload, resources, and perceived challenges. Results: Responding sites all report a designated SC. Figure 1 describes SC’s Status (full vs. part-time), onboarding, and resources. Of note, only 35% of SC’s manage stroke full-time at a single site, SC turnover rate is high with 63% in the role <4 yrs. Stroke coordinators abstract for multiple databases, even with data abstractor support. In free-text responses, 58% (19/33) of SC’s cited lack of time and/or corporate structure for adequate program management as the biggest challenges in their role. Discussion: The preliminary study highlights significant challenges with high SC turnover, heavy workloads, and insufficient support. Stroke programs lack clear recommendations from certifying bodies for program personnel based on program volume. Future directions of FSR HIS include conducting additional dissemination waves, and an analysis of optimal stroke program staffing by cross-referencing certification level, patient volume and SC resources.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".