Prevalence and associated factors of alexithymia among medical students: A cross-sectional study from Saudi Arabia
Bibliographic record
Abstract
Objectives: To assess the prevalence of alexithymia and its associated factors among medical students at King Saud University (KSU), Riyadh, Kingdom of Saudi Arabia. Methods: A cross-sectional study was conducted at KSU, including 420 medical students from all years of medical college (i.e., first to the fifth year), by using an electronic questionnaire distributed during August 2021. The questionnaire consisted of sociodemographic-related questions and the 20-item Toronto alexithymia scale (a validated scale in the literature). Results: The prevalence of alexithymia among the participants was found to be 26.9%. A statistically significant association between alexithymia and gender (p=0.013) was found. A diagnosis with any psychiatric condition (p=0.026), history of abuse during childhood (p=0.006), and lack of physical activity were associated with alexithymia. Conclusion: The prevalence of alexithymia among medical students at KSU was significantly higher than general population in literatures. It was indicated in the results that being female, having a psychiatric condition or history of childhood abuse, and lack of physical activity were all associated with alexithymia. We recommend increasing awareness of and screening for alexithymia and its associated factors among medical students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".