Neurophobia Inception: A Study of Trainees' Perceptions of Neurology Education
Bibliographic record
Abstract
OBJECTIVES: We wanted to examine the extent to which "neurophobia" exists among medical students and determine if students' perceptions of neurology differ by year of study while exploring the factors that contribute to the development of "neurophobia". METHODS: We used a two-phase, sequential, mixed-methods explanatory design in this single centre study. Phase 1 involved the collection and analysis of a questionnaire administered to students in the first three years of medical school. Phase 2 involved focus groups of a subgroup of students who demonstrated evidence of neurophobia in Phase 1. RESULTS: In total, 187 (39 %) undergraduate medical trainees responded to the questionnaire (response rates of 37%, 44% and 19% for first-, second- and third-year students, respectively). 24% of respondents indicated that they were afraid of clinical neurology and 32% were afraid of the academic neurosciences. Additionally, 46% of respondents thought that clinical neurology is one of the most difficult disciplines in medicine. Phase 2 findings revealed that many students reported negative preconceptions about neurology and commented on neurology's difficulty. Some experienced changes in these conceptions following their neurology block. Past clinical, educational, and personal experiences in neurology impacted their comfort level. CONCLUSIONS: This study shows that the level of comfort towards clinical neurology increases following students' participation in second-year neurology blocks, but that third-year students continue to show signs of neurophobia with lower comfort levels. It provides insight into why neurophobia exists amongst medical students and sheds light on pre-existing and emerging factors contributing to this sense of neurophobia.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".