Prevalence of Alexithymia and the influencing factors among medical students at Umm Al-Qura University: A cross-sectional study
Bibliographic record
Abstract
Background: Alexithymia is inability of the person to describe his emotions, somatic sensations, and struggle to discuss feelings.Objectives: To determine the prevalence of the state of alexithymia among undergraduate medical students and to explore its potential risk factors.Methods: A cross-sectional study was done through online survey targeted to undergraduate medical students.The survey included the Toronto Alexithymia Scale (TAS-20), students' socio-demographics, and the potential risk factors for alexithymia.Results: A total of 317 students participated in the study.A 56.5% prevalence of alexithymia among participants was demonstrated.A binary logistic regression model revealed higher risk of alexithymia among students with female gender (OR: 2.32, 95% CI: 1.47-3.65;p <0.001), divorced parents (OR: 3.23, 95% CI: 1.43-7.32;p = 0.005), history of psychiatric illness (OR: 3.40, 955 CI: 1.51-7.67;p = 0.003), and history of childhood emotional, physical and/or sexual abuse (OR: 2.46, 95% CI: 1.42-4.29;p=0.001).A lower risk for alexithymia was demonstrated among students with higher grade point average (OR: 0.32, CI: 0.04-0.93;P = 0.035).There was no association between alexithymia and students' academic year of study.Conclusions: The current study revealed high alexithymia prevalence among undergraduate medical students.The condition is linked with female gender, divorced parents, history of psychiatric illness, and childhood abuse, and is associated with lower academic performance.Accordingly, for prevention and proper intervention of alexithymia among medical students, students' screening for the condition and ease of their access to psychiatric care is recommended.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".