“Assessing neurophobia: a comparative study of awareness and prevalence among medical students at Badr University in Cairo and Helwan University”
Bibliographic record
Abstract
Abstract Introduction Neurophobia, defined as a fear or aversion toward neuroscience and clinical neurology study, was first introduced by Dr. Ralph Jozefowicz in 1994. This study aimed to assess the prevalence and contributing factors of neurophobia among clinical-year medical students at Badr University in Cairo (BUC) and Helwan University. Given the high incidence of neurological disorders in Egypt, addressing neurophobia is crucial to encourage medical students to pursue neurology and help meet the country’s growing healthcare needs. Methods A cross-sectional study was conducted on 1,235 clinical-year medical students from BUC and Helwan University. Quantitative data were collected using the Schon questionnaire and NeuroQ scale. Additionally, focus group discussions (FGDs) involving 62 students provided qualitative insights. Results Neurophobia was more prevalent among BUC students (47.7%) compared to Helwan students (26%). Male students reported higher levels of neurophobia, yet expressed greater confidence in neurology than female students. The perceived complexity of neuroanatomy (43.4%), lack of clinical exposure (52.5%), and excessive theoretical content (40.3%) were the main contributing factors. Early clinical exposure and more interactive teaching methods were strongly preferred by the students. Conclusion Neurophobia represents a major educational barrier and contributes to the global shortage of neurologists. A little is known regarding neurophobia among Egyptian medical students. Proposed solutions include adopting active learning strategies and reducing the time gap between neuroscience and neurology to enhance students’ confidence to reduce neurophobia among undergraduates. Yet, more studies are needed to reveal more about neurophobia prevalence, factors, and suggested plans to overcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".