Unveiling neurophobia: exploring factors influencing medical students, residents and non-neurologist physicians globally and its implications on neurology care – a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Neurophobia, the fear of neurology, is a recognised global challenge in medical education and practice. This systematic review and meta-analysis aimed to quantify the prevalence of neurophobia among medical students, residents and non-neurologist physicians, identify contributing factors (including lack of basic science/clinical integration) and explore its implications for neurology care. Methods: We systematically searched PubMed, Scopus and Google Scholar for studies published between 2000 and 2024 reporting on neurophobia. Two independent reviewers screened the studies, extracted data and assessed their quality using the Newcastle-Ottawa Scale. A random effects meta-analysis was performed to estimate the pooled prevalence of neurophobia. Heterogeneity and publication bias were tested statistically. Results: Of the initial 1245 studies, 32 met the inclusion criteria. The pooled prevalence of neurophobia was 47.2% (95% CI: 39.8% to 54.6%), with significant heterogeneity (I²=98.7%, p<0.001). Subgroup analysis revealed a higher prevalence among medical students (52.3%, 95% CI: 44.1% to 60.5%) than residents and physicians (41.9%, 95% CI: 33.7% to 50.1%). Key contributing factors included the perceived complexity of neurology (OR: 3.2, 95% CI: 2.7 to 3.8) and inadequate exposure during training (OR: 2.8, 95% CI: 2.3 to 3.3). Individuals with neurophobia were less likely to consider a career in neurology (OR 0.32, 95% CI: 0.25 to 0.41). Conclusions: Neurophobia affects a substantial proportion of medical trainees and practitioners globally, with variation across education and practice levels. Addressing contributing factors through targeted interventions may help mitigate neurophobia and improve neurological care. Further studies should focus on specific interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".