Distinguishing between primary and secondary callous-unemotional features in youth: The role of emotion regulation
Bibliographic record
Abstract
Background: Research on youth with callous-unemotional features (CU features; e.g., lack of empathy) has historically categorized these behaviors as biologically-driven and homogeneous across development.However, an early model proposed that two subtypes of CU features exist with different etiological factors.The first, or 'primary' group, has a genetically based deficit in emotion processing, resulting in a diminished sensitivity to others' emotional cues.The 'secondary' CU group is conceptualized as an adaptation to environmental factors such as maltreatment and are characterized by an affective deficit produced by these powerful environmental factors.Secondary youth are typically classified or grouped based on the presence of co-occurring anxiety symptoms.Understanding the presentation of regulation strategies among CU variants may give us further insight into the different pathways to their development.In addition, due to the high number of samples that have relied on justice-involved males, there is a paucity of research on gender differences across the variants. Purpose:The aim of the current study was to evaluate whether distinct groups of youth may be identified in a clinical sample by using measures of affect dysregulation and suppression, anxiety symptoms and experience of maltreatment.It was also to examine whether these distinct groups were consistent across males and females. Method:Participants (N = 418; 56.7% female) ranged in age from 12 to 19 (M = 15.04,SD = 1.85) and were drawn from the baseline of a large clinical sample. Results:A Latent Profile Analysis (LPA) was conducted using five indicators including affect regulation, suppression, anxiety, CU features, and maltreatment.The best fitting model, a 4-class solution, had a significant Lo-Mendell-Rubin (p= .003),an acceptable entropy score (.78), and classification probabilities that suggested accuracy and good separation.The four groups to emerge included a low, anxious, primary CU, and secondary CU group .Gendered LPAs found a 4-class model fit for both males and females (entropy= .866).Gender did not moderate other outcomes of interest.Discussion: This study extends previous literature by including the underlying process of dysregulated affect to the model in identifying primary and secondary subgroups and examining gender.Clinical implications are discussed.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".