The Depressive Experiences Questionnaire: construct validity and prediction of depressive symptoms in a sample of Chinese undergraduates
Bibliographic record
Abstract
BACKGROUND: The Depressive Experiences Questionnaire (DEQ) was developed to measure two dimensions of depression-prone personality, Dependency and Self-criticism. We investigated the construct validity and prediction of DEQ in a Chinese sample, and explored whether Blatt's conceptualizations of depression-prone personality variables are appropriate for the Chinese context. METHODS: The original version of the DEQ was translated into Chinese (DEQ-C). During the initial assessment, 640 Chinese university students completed the DEQ-C and the Center for Epidemiologic Studies Depression Scale (CES-D). Six months later, the CES-D was re-administered. RESULTS: A principal components analysis yielded a three-factor model that was consistent with Blatt's theory. However, these three factors emerged in a different order in comparison to the original sample. Factorial validity was also acceptable with low correlations between each DEQ-C factor in males (r=.01 approximately -0.14), and females (r=0.19 approximately 0.28). Convergent validity was supported by significant positive correlations between the CES-D and both Dependency and Self-criticism. Predictive validity was demonstrated by hierarchical multiple regression analyses showing that Self-criticism predicted increased depressive symptoms both in males (beta=0.27, p<0.01) and in females (beta=0.16, p<0.05); Dependency predicted levels of depressive symptoms only in females (beta=0.11, p<0.05). CONCLUSIONS: The Chinese version of the DEQ demonstrated satisfactory validity, including construct validity and predictive validity, the DEQ-C can be considered as an appropriate tool for assessing personality vulnerability to depression in Chinese college 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 | 0.001 |
| 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".