Assessing maladaptive traits in youth: An English-language version of the Dimensional Personality Symptom Itempool.
Bibliographic record
Abstract
The present study addresses the psychometric properties of the English version of the Dimensional Personality Symptom Item Pool (DIPSI), a comprehensive taxonomy of trait-related symptoms in childhood. The structural invariance of the English DIPSI and the original Flemish version was investigated in a large sample of Canadian (n = 341) and Flemish (n = 509) adolescents, using both self- and maternal ratings. The original 4-factor structure of the DIPSI, including the dimensions Emotional Instability, Disagreeableness, Introversion, and Compulsivity, was replicated in the Canadian sample across informants. Results provided support for metric invariance across the English and Flemish DIPSI version, indicating that associations between variables across samples can be confidently made, although the meaning of specific items may slightly differ across the different DIPSI versions. Across raters, the Flemish and English DIPSI dimensions showed a similar covariation pattern with internalizing and externalizing psychopathology. High intercorrelations between the DIPSI dimensions in both the Flemish and English version suggest low discriminant validity, potentially resulting from lower variance on personality pathology in general populations, from a general pathology factor, or from developmental issues. To conclude, the English version of the DIPSI can be considered a promising tool for assessing maladaptive traits in younger age groups in internationally oriented research designs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".