Alexithymia Among Medical Students and Its Influencing Factors: A Latent Profile Analysis
Bibliographic record
Abstract
Background Alexithymia, the difficulty in identifying and expressing emotions, has been identified as a potential factor influencing mental health in various populations, including medical students. Understanding the prevalence and influencing factors of alexithymia in this population is crucial for addressing their emotional well‐being and academic performance. Objective The aim of this study was to explore the presence of alexithymia among medical students and to identify the factors that contribute to its development. Methods A total of 780 medical students from one medical university participated in the study. Participants were assessed using standardized measures of alexithymia and other relevant psychological scales. Latent profile analysis (LPA) was employed to identify distinct profiles of alexithymia based on the data. Results Three distinct profiles of academic burnout were identified. Significant factors influencing profile membership included residence, psychological resilience, and emotion regulation ability ( p < 0.05). Conclusions This study identifies the heterogeneity of alexithymia among medical students and highlights significant factors that contribute to its development. Understanding these profiles can help in developing targeted interventions to improve emotional awareness and mental health among medical students.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".