P.032 What do elective students learn about the specialty of Neurology (and what can that teach us)?
Bibliographic record
Abstract
Background: “Neurophobia” describes a fear of Neurology on the part of medical students. This contrasts with the “neurophilia” that exists in society with increasing awareness of disorders such as stroke and multiple sclerosis. Ideally, we should take advantage of “neurophilia” to promote our specialty’s strengths. One step would be to better understand what students learn from a Neurology elective. Methods: This was a qualitative study. Students completing an elective between September 2011 and March 2015 at the Jewish General Hospital (JGH) in Montreal completed written pre- and post-elective questionnaires. Results: 36 students participated; 15 from McGill, 11 from other Canadian medical schools, and 10 from International medical schools. Many students changed their opinion about Neurology, with fewer citing lack of treatments or poor patient prognoses as negatives after completing their elective. They valued knowledge acquired about the neurological exam and problem-solving, while the range of cases and subspecialties surprised them. Many would diversify the setting of their elective to better experience this variety. Conclusions: More diversified elective experiences could showcase the strengths of our specialty and the scope of neurological practice. Presenting Neurology as a challenging, intellectually stimulating specialty that emphasizes problem solving could increase student interest.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.008 |
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".