Abstract W P356: Neuroscience ICU Advanced Practice Provider Competency Model
Bibliographic record
Abstract
Background: A large community based stroke program began utilizing advanced practice providers (APPs) 24/7 in the 18-bed Neuroscience ICU. The nurses needed 24/7 in-house support in managing these complex patients. Literature shows that APPs impact the length of stay and health care cost. This practice change led to an evaluation of the process of educating and ensuring competency to the APPs. Purpose: The goal was to ensure competency of APPs demonstrated by complete understanding of complex disease management and ongoing skills to manage critical neuro patients. Methods: Core Competency models were designed and used as a framework to aid in critical care specialization. Advanced classes, applicable clinical rotations, and procedure based skills lab were utilized to train the APPs. Initial measures to evaluate efficacy of the model included 14-item knowledge tests administered before and after a critical care symposium. Two versions of the test were administered using a crossover design, and changes in test scores were examined using analysis of variance. Additional competency measures included procedure and skill check-off competencies, neuro-monitoring utilization, provider sign-offs and one-on-one education by system educators. Results: APPs scored significantly higher on the post-test than the pre-test (mean difference = 19.71%, CI:14.3%-25.2%; p-value <0.0001), suggesting the symposium advanced their neuroscience knowledge. There was no difference in scores between the two versions of the test (p=0.98). Strict adherence to integration of current evidence based practices combined with an in-depth orientation is critical to ensuring clinical competence. Program focus includes facilitating ongoing education in a variety of neuroscience topics via lecture and clinical experiences to advance knowledge to the highest level within their Scope of Practice. Conclusions: Utilizing a combination of specially trained critical care physicians, 24/7 accessible neurologists, neurosurgeons and neuro interventionalists on the training of the APPs for this complex patient population is the key to the success of the neurocritical care APP competency model.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".