Use of the Personality Assessment Inventory (PAI) in individuals with traumatic brain injury
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To evaluate the extent to which the Personality Assessment Inventory (PAI) is confounded by symptoms that are transdiagnostic between psychopathology and neurological sequelae. METHODS: Sixty-one adults with moderate-to-severe traumatic brain injury (TBI) completed the PAI over the first year post-injury. Items that discriminated brain-injured individuals from a normative sample were identified using effect size analyses and were then subjected to principal components analysis (PCA) with varimax rotation. To explore whether the items identified in the PCA may be considered transdiagnostic in nature, an expert rating task and correlations with objective outcome measures were employed. RESULTS: Effect sizes analyses identified 21 items that discriminated adults with TBI from the normative sample. Eight items associated with component 1 of the PCA were considered to be transdiagnostic. These items reflected health concerns and thinking problems from the Somatic Complaints, Depression and Schizophrenia scales. Items belonging to the other components reflected behaviours that are commonly associated with TBI, but not considered transdiagnostic. CONCLUSION: Using a comprehensive and multi-modal approach, results demonstrated good convergent validity for a small sub-set of items as being transdiagnostic. Overall, the findings support the PAI as a useful measure of psychiatric and emotional disturbances among persons with TBI.
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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 | 0.009 |
| 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) | 0.001 | 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".