Childhood predictors of cluster A personality disorder traits in adolescence: a seven-wave birth cohort study
Bibliographic record
Abstract
Cluster A personality disorders are hypothesized to have their origins in childhood, but little prospective research exists to support this contention. We investigated whether factors intrinsic to the child, social-relational and environmental factors, and symptoms of other psychopathologies in childhood predict paranoid, schizoid, and schizotypal personality disorder traits at age 16. A sample from two birth cohorts in Trondheim, Norway (n = 1,077; 50.9% female) was examined biennially from age 4-16. Cluster A personality disorder traits were assessed with the Structured Clinical Interview for DSM-5 Personality Disorders and regressed on the intercept and growth in child risk and protective factors up to age 14. The prevalence of any Cluster A PD at age 16 was 2.41% (95% CI: 1.12, 3.69); paranoid 1.36% (CI: 0.42, 2.31); schizoid 0.56% (CI: -0.11, 1.23); schizotypal 1.05% (CI: 0.23, 1.87). Elevated and rising levels of odd or eccentric behavior, heightened and increasing neuroticism, low conscientiousness, declining self-esteem, and growing emotional and behavioral difficulties predicted both paranoid and schizotypal features, whereas low and rising levels of insecure attachment predicted paranoid traits only. Schizotypal traits also shared early risk factors with schizoid traits: Problems with emotion regulation and cluster A traits in parents. Several assumed predictors were unpredictive: Having an imaginary friend, disorganized attachment, negative life-events, and autism spectrum symptoms. In conclusion, cluster A traits at age 16 can be predicted by a range of factors already evident in childhood and early adolescence, most notably oddity, symptoms of emotional and behavioral disorders, low self-esteem, social withdrawal, and personality traits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".