Childhood maltreatment and personality disorders in adolescents and adults with psychotic or non-psychotic disorders
Bibliographic record
Abstract
Introduction While the attention to personality disorders (PD) and childhood maltreatment (CM) has grown in recent years, there remains limited understanding of the prevalence and distinctions of PD and CM in clinical populations of Chinese adolescents in comparison to adults. Methods A total of 1,417 participants were consecutively sampled from patients diagnosed with either psychotic or non-psychotic disorders in the psychiatric and psycho-counseling clinics at Shanghai Mental Health Center. The participants were categorized into two groups based on their age: adolescents (aged 15-21 years) and adults (aged 22-35 years). PDs were evaluated using a self-reported personality diagnostic questionnaire and a structured clinical interview, while CMs were assessed using the Chinese version of the Child Trauma Questionnaire Short Form. Results When comparing self-reported PD traits and CM between adolescents and adults, differences emerge. Adolescents, particularly in the psychotic disorder group, exhibit more pronounced schizotypal PD traits (p=0.029), and this pattern extends to non-psychotic disorders (p<0.001). Adolescents in the non-psychotic disorder group also report higher levels of emotional abuse (p=0.014), with a notable trend in physical abuse experiences compared to adults (p=0.057). Furthermore, the most prevalent PDs in the clinical sample are avoidant, borderline, and obsessive-compulsive PDs. Among patients with psychotic disorders, adolescents exhibit higher rates of schizoid, schizotypal, and obsessive-compulsive PDs compared to adults. Logistic regression analyses highlight distinct predictors for psychotic and non-psychotic disorders in adolescents and adults. Discussion The findings emphasize distinct differences in PDs and CMs between adolescent and adult groups, shedding light on their potential roles in psychotic and non-psychotic disorders.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".