Spectrum of Anxiety Disorders Among Medical Students in a Nigerian Medical School: A Cross-Sectional Study With Standardized Screening Tools
Bibliographic record
Abstract
BACKGROUND: Anxiety disorders among medical students constitute a global problem, and also reflect the mental state of the general population. There is paucity of data on the spectrum of such disorders among medical students in Nigeria.AIM: The study aims to determine the prevalence of anxiety disorders among medical students, and the effect of socio-demographic characteristics.METHODS: A total of 217 medical students from the second to the final years of study at Enugu State University of Science and Technology in south-east Nigeria were enrolled by simple random sampling. Five pretested, self- administered standardized questionnaires were used as screening tools for anxiety disorders. Data were analyzed using the Statistical Package for Social Sciences program (SPSS version 20). A p-value less than 0.05 was taken as statistically significant.RESULTS: Thirty one (14.3%) of the enrolled medical students fulfilled the screening criteria for anxiety disorders. Specifically, generalized anxiety disorder (GAD) was significantly related to gender (p =0.017) and the year of study (p =0.017). Post-traumatic stress disorder (PTSD) was significantly related to the year of study (p =0.037), and social anxiety disorder (SAD) to the year of study (p =0.003) and gender (p =0.04). Similarly, panic disorder was significantly related to the year of study (p =0.025) while specific phobia was significantly associated with marital status (p =0.003), parental monthly income (p =0.022) and student’s monthly allowance (p =0.002). Finally, obsessive-compulsive disorder was significantly related to marital status (p =0.034) and year of study (p =0.028).CONCLUSION: Medical students in Nigeria are prone to a spectrum of anxiety disorders. This susceptibility is influenced by socio-demographic characteristics.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".