P.050 Understanding the neurocritical care educational needs of trainees
Bibliographic record
Abstract
Background: Patients with neurological conditions account for 15% of patients admitted to the Intensive Care Unit (ICU). Neurocritical care (NCC) has been proven to reduce mortality, improve functional outcomes and increase patient/family satisfaction. Trainees often lack the knowledge, skills, and experience needed to provide quality NCC. Consequently, timely effective care is compromised, team dynamics suffer, and trainees may experience distress. Methods: To fully understand educational needs, we surveyed University of Calgary residents from various programs who rotate through the Neuro-ICU. Results: Trainees indicated a lack of confidence in their knowledge and skills of most NCC disorders/scenarios in the ICU. While the majority expressed interest in learning NCC, 58% were not aware of the NCC-related competencies outlined for their specialties by the Royal College of Physicians and Surgeons, and 30% had no objectives of their own. Teaching modalities most preferred included patient-centred bedside teaching (96%) and easily accessible resources such as pocket-sized cards (90%) and/or a phone app (96%). Conclusions: Trainees rotating through Neuro-ICU need more accessible and improved learning resources and tools. An NCC curriculum may help improve patient outcomes, team dynamics, and relieve trainee distress.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.090 | 0.005 |
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".