Cognitive vulnerability to depressive symptoms in adolescents in urban and rural Hunan, China: A multiwave longitudinal study.
Bibliographic record
Abstract
The current multiwave longitudinal study examined the applicability of two cognitive vulnerability-stress models of depression-Beck's (1967, 1983) cognitive theory and the hopelessness theory (Abramson, Metalsky, & Alloy, 1989)-in two independent samples of adolescents from Hunan Province, China (one rural and one urban). During an initial assessment, participants completed measures assessing dysfunctional attitudes (Beck, 1967, 1983), negative cognitive style (Abramson et al., 1989), neuroticism (Costa & McCrae, 1992), depressive symptoms, and anxiety symptoms. Once a month for the subsequent 6 months, participants completed measures assessing the occurrence of different types of negative events, depressive symptoms, and anxiety symptoms. Results provided support for cognitive vulnerability factors as predictors of increases in depressive symptoms following the occurrence of higher than average levels of negative events in Chinese adolescents. The results also supported the specificity of these two cognitive vulnerability factors as predictors of depressive versus anxiety symptoms following the occurrence of higher than average levels of negative events (i.e., symptom specificity), and the ability of cognitive vulnerability factors to predict prospective change in depressive symptoms above and beyond the effects of trait neuroticism (i.e., etiological specificity).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".