The association among negative life events, alexithymia, and depressive symptoms in a psychosomatic outpatient sample
Bibliographic record
Abstract
BACKGROUND: Depression is a life-threatening mental health problem. Various factors have been demonstrated to be associated with depressive symptoms, including negative life events (NLEs) and alexithymia. A retrospective study was conducted to investigate the relationship among negative life events, alexithymia, and depression symptoms in a psychosomatic outpatient sample in China. METHODS: A total of 2747 outpatients (aged 18 - 65) were included in this investigation. The Life Events Scale (LES), Toronto alexithymia scale (TAS-26), and 9-item Patient Health Questionnaire (PHQ-9) were used to assess NLEs, alexithymia, and depressive symptoms, respectively. A stepwise regression analysis model was established to investigate the relationship among alexithymia, NLEs, and depressive symptoms. RESULTS: Overall, 67.0% of the patient sample had a PHQ-9 score of 10 or higher. The stepwise regression analysis model showed a well-fitted model, in which NLEs and alexithymia explain a total of 34.2% of the variance of depressive symptoms in these participants. NLEs (β = 0.256, p < 0.001) and dimensions of alexithymia (difficult describing feelings (β = 0.192, p < 0.001) and identifying feelings (β = 0.308, p < 0.001)) were positively correlated with symptoms of depression. CONCLUSIONS: Previous studies have confirmed the correlation between NLEs and depression, alexithymia and depression, respectively. In our study, we used a stepwise regression model to explain the relationship among those variables simultaneously, and found that NLEs and alexithymia could function as predictors of depressive symptoms. Based on this discovery, alexithymia-focused treatment strategies could be alternative in depressive patients with alexithymia, but this remains to be verified in the future.
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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".