Personality Assessment Inventory (PAI): obsolete norms identify psychopathology in nearly everyone
Bibliographic record
Abstract
Background: The Personality Assessment Inventory (PAI), a self-report personality test, is one of the frequently used measures to assess psychopathology in a wide variety of settings including in high stakes assessments, for example, in child custody disputes, employment settings and fitness for duty assessments. The PAI has never been normed on a Canadian population and the PAI users have simply assumed that the US norms also describe the Canadian population. Moreover, accumulated research demonstrates that the PAI's 35 years outdated and obsolete norms no longer describe neither university students' nor normal adult US populations. Method: We administered the PAI to over 200 university students in a mid-size Canadian university. Results: Our students scored on average in moderately elevated range (60T to 69T) on many of the PAI scales including anxiety (ANX), anxiety-related disorders (ARD), depression (DEP), schizophrenia (SCZ), and borderline features (BOR). Multivariate base rate analyses revealed that approximately 95% of our sample scored in elevated range on at least one out of the 22 PAI Scales. Furthermore, although some of the PAI reliabilities are adequate for research, the PAI reliabilities are too low for using the PAI in high stakes and forensic assessment, for example, in insurance benefits, child custody, employment, and fitness for duty assessments. Discussion: We conclude that the PAI US norms are no longer appropriate for high-stakes assessments, ought to be withdrawn immediately, and new up-to-date norms ought to be established to prevent mislabelling and diagnostic misclassifications of and harm to examinees. Continued use of the PAI outdated norms in high stakes assessments carries ethical risks, is non-scientific, and likely amounts to malpractice.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".