P.133 ‘Building Your Neurology Acumen’: a flipped classroom approach to strengthen Internal Medicine residents’ neurological skills
Bibliographic record
Abstract
Background: Rotating internal medicine (IM) residents do not feel adequately prepared to approach patients with neurologic issues. The purpose of this project was to conduct a needs assessment to determine the optimal components and delivery of a neurology curriculum for internal medicine residents. Methods: We utilized a mixed-methods design and recruited participants through a combination of purposive and convenience sampling. We conducted interviews with IM residents (n=12) and focus groups with neurology residents (n=7) and neurology staff (n=8). IM residents completed entry- and post-call surveys while on a neurology rotation. Results: Themes according to Kern’s framework for curriculum development: 1. Problem: Discomfort and perception of under-preparedness amongst IM trainees 2. Needs Assessment: What the learners (stakeholders) think they need to know vs. what their teachers want them to know vs external requirements (Royal College) 3. Goals/objectives: What content is relevant for clinical requirements vs assessments? 4. Methods and setting: Didactic vs bedside vs virtual 5. Implementation of the curriculum 6. Evaluation and feedback Conclusions: Our findings illustrate a possible mismatch between internal medicine residents’ needs and neurologist teachers’ expectations in teaching neurology. Addressing learners’ needs could enhance neurology knowledge and sense of preparedness when encountering patients with neurologic issues.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".