P.011 Evaluation of educational needs in neurology in the province of Quebec: a survey-based study
Bibliographic record
Abstract
Background: In contrast with 56% of US medical schools, most Canadian medical schools do not offer a required clerkship neurology rotation. This study aims to assess the need for additional clinical neurology training in Quebec medical schools. Methods: Third and fourth year medical students from the province of Quebec completed surveys inquiring about accumulated theoretical teaching time, clinical neurology exposure, self-reported neurological examination proficiency and interest in additional training. Results: 66 students answered the survey. 43% were from Université de Montréal, 18 % from McGill University, 14% from Université Laval and 24% from Université de Sherbrooke. For theoretical teaching, 44% reported at least 60 hours (h) of teaching, 44% reported 40 to 60 h and 23% reported 10 to 40 h. For clinical exposure, 24% reported at least 60 h, 8% reported 40 to 60 h, 40% reported 10 to 40 h and 29% reported less than 10 h. Most students reported being comfortable with their neurological examination skills (58%) but still 41% were uncertain or felt uncomfortable. 80% indicated interest in receiving additional clinical exposure. Conclusions: Amongst Quebec medical students, clinical neurology exposure is likely insufficient. An important proportion of students remain uncomfortable with the neurological examination and most students are interested in additional neurological training.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".