The relationship between core self-evaluation and cognitive failure in Chinese adolescents: the sequential mediating role of alexithymia and depression
Bibliographic record
Abstract
BACKGROUND: The difficulties that cognitive failure can pose for individuals in the conduct of their everyday life have been documented in many studies. However, there is still limited understanding of the factors that influence cognitive failure and the mediating processes involved. This study uses cognitive resource theory to develop a chain mediation model in order to explore the relationship between core self-evaluation (CSE), alexithymia, depression, and cognitive failure. METHODS: Chinese middle school students (aged 14-18 years, 15.39 ± 0.58) were recruited as participants, and a total of 1,400 participants completed the Core Self-Evaluation Scale (CSES), Cognitive Failures Scale (CFS), Toronto Alexithymia Scale (TAS-20), and Depression Self-Rating Scale (SDS). SPSS 27.0 was used for common method bias testing, descriptive statistical analysis, correlation analysis, and sequence mediation analysis. RESULTS: Core self-evaluation (r = -0.52), alexithymia (r = 0.65), and depression (r = 0.57) were significantly correlated with cognitive failure, and core self-evaluation could significantly negatively predict cognitive failure (β = -0.06, p < 0.05). Alexithymia and depression played a partial mediating role between core self-evaluation and cognitive failure (CI = [-0.43, -0.33], effect = -0.38), specifically including three pathways: firstly, the independent mediating role of alexithymia (CI = [-0.28, -0.20], effect = -0.24); secondly, the independent mediating role of depression (CI = [-0.14, -0.07], effect = -0.10); thirdly, the sequential mediating role of alexithymia and depression (CI = [-0.05, -0.02], effect = -0.04). CONCLUSION: Core self-evaluation was significantly negatively correlated with cognitive failure. Alexithymia and depression played a partial mediating role between core self-evaluation and cognitive failure. The results indicate that raising core self-evaluation, addressing depression, and reducing alexithymia are crucial for reducing cognitive failure issues among adolescents. Therefore, schools and families can take some measures to provide more positive support for teenagers, help them form positive self-awareness, and reduce the occurrence of negative emotions and cognitive errors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".