Defining the Role of the Academic Neurohospitalist in Residency Education
Bibliographic record
Abstract
OBJECTIVE: We sought to better understand the potential impact of the burgeoning neurohospitalist model of inpatient care on education of neurology residents and to better define possible roles for "neurohospitalists" in residency education. METHOD: We designed a brief qualitative open-ended survey directed toward academic leaders in neurology and distributed it by e-mail to every academic neurology department in the United States and Canada. RESULTS: Of 83 respondents, 36 (43%) had an active neurohospitalist program and only 10% felt certain they would not have 1 within the next 5 years. All respondents expected to have residents continue to be involved with inpatient care. The main perceived advantage for resident education associated with neurohospitalists was inpatient care expertise, and the main expected disadvantage was decreased exposure to subspecialty attendings. The majority anticipated positive impact on all Accreditation Council for Graduate Medical Education core competencies predominantly based on neurohospitalists' expertise in the inpatient setting. CONCLUSION: The majority of academic neurology departments are expected to have a neurohospitalist program within the next 5 years. There are several perceived advantages and disadvantages to such a program for education of neurology residents. In general, the impact of these programs is expected to improve resident education. Regardless of expectations, neurohospitalists will likely play a prominent role in the education of the next generation of neurologists.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".