Discriminating malingered attention Deficit Hyperactivity Disorder from genuine symptom reporting using novel Personality Assessment Inventory validity measures
Bibliographic record
Abstract
It is now widely understood that ADHD can be feigned easily and convincingly. Despite this, almost no methods exist to assist clinicians in identifying when such behavior occurs. Recently, new validity indicators specific to feigned ADHD were reported for the Personality Assessment Inventory (PAI). Derived from a logistic regression, these algorithms are said to have excellent specificity and good sensitivity in identifying feigned ADHD. However, these authors compared those with genuine ADHD only to nonclinical undergraduate students (asked to respond honestly or asked to simulate ADHD); no criterion group of definite malingerers was included. We therefore investigated these new validity indicators with 331 postsecondary students who underwent assessment for possible ADHD and compared scores of those who were eventually diagnosed with ADHD (n = 111) to those who were not [Clinical controls (66), Definite malingerers (36); No diagnosis (117)]. The two proposed PAI algorithms were found to have poor positive predictive value (.19 and .17). Self-report validity measures from the Connors’ Adult Attention Rating Scale, and the Negative Impression Management scale on the PAI returned more positive results. Overall, more research is needed to better identify noncredible ADHD presentation, as the PAI-based methods proposed by Aita et al. appear inadequate as symptom validity measures.
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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 | 0.014 |
| 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".