Associations between callous-unemotional traits and psychopathology in a sample of adolescent females.
Bibliographic record
Abstract
Background: Callous-unemotional (CU) traits (e.g., lack of empathy and guilt, deficient affect) have been associated with severe and persistent patterns of conduct problems and antisocial behaviour as well as with poorer treatment outcomes. They are now a specifier to the diagnosis of conduct disorder. Objective: To examine the associations between CU traits and a wide set of psychopathological symptoms (e.g., anxiety, conduct disorder) in a sample of adolescent females. Method: = 50) self-reported on their levels of CU traits and psychopathological symptoms. Results: Participants recruited from high schools, compared to their counterparts from the youth center, had lower scores on most of the scales and subscales of CU traits and psychopathology. The total score of CU traits as well as the callousness-uncaring dimension were correlated with externalizing symptoms for the participants from the schools and from the youth center. However, the total score of CU traits as well as the unemotional dimension were correlated with internalizing symptoms especially among participants from the schools. Conclusions: Our analyses revealed differences in the patterns of associations depending on the subscales of CU traits and across sample types (i.e., school subsample versus youth center subsample), which should be considered in the assessment of psychopathology in these populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".