Callous-Unemotional Traits in Adolescents' Daily Life: Associations with Affect and Emotional and Conduct Problems
Bibliographic record
Abstract
Abstract Intensive longitudinal methods (e.g., daily diary) inform understanding of dynamic processes by parsing within-person state-like fluctuations from stable between-person trait-like differences. In this exploratory study, we investigated whether self-reported callous-unemotional (CU) traits (callousness, uncaring) demonstrated daily fluctuations, as well as whether daily CU traits were associated with multiple forms of daily emotional and behavioral functioning. A sample of 99 adolescents (55.8% female; Mage = 14.60 years) provided baseline information and completed a naturalistic 30-day diary reporting on CU traits, positive and negative affect, and emotional and conduct problems in their daily lives. Dynamic structural equation modeling revealed that many CU traits items showed within-person autoregressive and cross-lagged links; however, there was substantial between-person variation in within-person fluctuations across items. At the subscale level, cross-day associations were observed between callousness and uncaring, conduct problems and uncaring, positive affect and callousness, negative affect and emotional problems, and emotional problems and negative affect. By harnessing intensive longitudinal data, our findings provide preliminary state-level evidence of CU traits, as well as functional information with regards to CU traits and emotional and behavioral problems in daily life. We consider the implications of our findings in terms of informing future CU traits intensive longitudinal evaluations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".