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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".