Prevalence of Alexithymia and Associated Factors Among Dental Students in Saudi Arabia: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Mental health challenges among university students are pervasive, with alexithymia posing a particularly significant yet understudied challenge. This condition significantly affects an individual’s ability to cope with stress due to difficulties in recognizing, describing, and processing emotions. Objectives: This study aims to evaluate alexithymia prevalence and its associated factors among dental undergraduate students and interns enrolled at King Saud University in Riyadh, Saudi Arabia. Methods: Data were collected through a self-administered online survey that assessed alexithymia symptoms (using the Toronto Alexithymia Scale [TAS-20]), sociodemographic profiles, lifestyle-related factors, and health-related factors. The associations between participant factors and alexithymia were assessed using chi-square and multiple logistic regression analyses. Results: Of the 421 eligible participants, 369 completed the survey (87.6% response rate), revealing a significant prevalence of alexithymia (37.9%). Female gender (AOR = 1.7, p = 0.04), depression (AOR = 5.6, p < 0.0001), chronic diseases (AOR = 3.5, p = 0.003), and childhood abuse (AOR = 2.2, p = 0.047) were independent factors significantly associated with alexithymia. Conclusions: These findings highlight the pressing need for mental health support within dental education. Early interventions targeting alexithymia could mitigate its adverse consequences, promoting better student well-being and academic success.
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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 | 0.001 |
| 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".