A psychometric examination of the PAI-SF in persons with recent stroke
Bibliographic record
Abstract
Objective The present study evaluated the psychometric properties of the Personality Assessment Inventory-Short Form (PAI-SF) for use with patients with recent stroke. Method: Study participants (N = 170) were inpatients in a tertiary hospital in Western Canada admitted to a rehabilitation department who completed a neuropsychological evaluation as part of their care. All participants completed the full-form of the PAI (344 items) and both full- and short-form (160 items) versions were scored from the same protocol. Results: Internal consistency for the PAI-SF scales was assessed by Cronbach's coefficient alpha. Alpha coefficients for clinical scales fell between the range of 0.53 (ANT) to 0.88 (ANX), with three scales (ANT, ALC, and DRG) falling below satisfactory (<0.70). Alpha coefficients were unsatisfactory for validity, treatment, and interpersonal scales. Absolute differences between mean clinical scale t scores between the full and short-form PAI clinical scales ranged from 0.04 (DEP) to 1.18 (MAN). For an individual, absolute differences in scale t scores between the full- and short-forms ranged from 0 to 30 t scores. On average, an individual varied 3.75 t scores between the PAI full- and short-form across all validity, clinical, interpersonal, and treatment scales. Component structure was similar across the full- and short-forms. Conclusions: Findings are somewhat consistent with previous literature on the PAI-SF as the full- and short-forms had minimal differences and similar psychometric properties. However, caution is warranted for the clinical utility for both forms given the lower alpha coefficients and different structure. Only certain clinical scales appear to have strong psychometric properties.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".