Latent profile of alexithymia in Chinese nursing students and its relationship with anxiety, depression, and stress: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: In this study, we aimed to explore the potential types of alexithymia in nursing students and their relationships with depression, anxiety, and stress and provide a reference for formulating targeted intervention programs. Understanding the mental health status of nursing students is critical for preparing the next generation of nurses and improving the quality of nursing services. Therefore, understanding the different subtypes of alexithymia in nursing students and their relationships with depression, anxiety, and stress is crucial. METHODS: This study employed a cross-sectional survey design. Socio-demographic questionnaire for nursing students was used alongside the Toronto alexithymia scale (TAS-20) and Depression Anxiety Stress Scale (DASS-21). Latent profile analysis was used for identifying potential alexithymia categories in nursing students and analyzing the relationships between these alexithymia categories and depression, anxiety, and stress. RESULTS: Among the participants, 7.63% (29/380) of the students had an affective disorder score ≥ 61; depressive symptoms were present in 16.6% (63/380) of the participants. Anxiety symptoms were reported by 22.6% (86/380) of nursing students. Stress symptoms were identified in 4.2% (16/380) of the sample, indicating a significant need for addressing the psychological state of nursing students. Alexithymia in nursing students can be categorized into three groups: without alexithymia, reflected by low difficulty identifying feelings (DIF) and difficulty describing feelings toward others (DDF) scores (29%); the medium-risk group of alexithymia, reflected by high DIF and DDF scores (37.5%); and the high-risk group of alexithymia, (33.5%). Nursing students without alexithymia, reflected by lower DIF and difficulty describing feelings toward others (DDF) scores, had lower risks of depression, anxiety, and stress than did nursing students who might have alexithymia (OR = 0.054, 0.075, 0.052; P < 0.05). CONCLUSION: Three distinct characteristics of alexithymia among the nursing students were identified. Those with higher alexithymia scores reported significantly higher levels of anxiety, depression, and stress symptoms. Routine screening for alexithymia might help identify students at a higher risk of psychological distress; further research on targeted support strategies is warranted. Although longitudinal studies are needed for establishing temporal relationships, these results emphasize the importance for considering emotional abilities in mental health initiatives for nursing students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".