Exploring the Prevalence of Alexithymia Among Medical Students and Its Impact on Their Academic Performance: A Cross-Sectional Study in Peshawar, Pakistan
Bibliographic record
Abstract
Transitioning from high school to medical college presents various psychological challenges for students, including alexithymia, a condition characterized by difficulties in identifying and expressing emotions. This study investigates the prevalence of alexithymia among medical students in Peshawar, Pakistan, and explores its potential impact on academic performance. Objective: To determine the prevalence of alexithymia among medical students, compare alexithymia levels across different academic years, and assess its association with academic performance. Methods: A descriptive cross-sectional study was conducted at Khyber Medical College, Peshawar, from September 2023 to September 2024, involving 227 medical students. Alexithymia was assessed using the Toronto Alexithymia Scale (TAS-20). Academic performance was measured based on annual marks and attendance. Data were analyzed using descriptive statistics, t-tests, and chi-square tests. Results: The study found that 122 (52.4%) students exhibited alexithymia, with a higher prevalence among female students (57.3%, n = 70 out of 122). Despite the notable prevalence of alexithymic traits, no significant association was observed between alexithymia and academic performance (p = 0.47). Most participants reported that emotional difficulties did not affect their studies, and very few sought psychiatric consultation or took leave due to emotional disturbances. Conclusion: Alexithymia is prevalent among medical students in Peshawar, particularly among females. However, the lack of a significant impact on academic performance suggests a level of resilience in students. Further research into coping strategies and support systems is recommended. Addressing alexithymia within medical education may improve both emotional well-being and academic outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".