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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".