An Inventory of Problems (IOP) Study of Symptom and Performance Validity in a Sample of Driver’s License Renewal or Reinstatement Applicants
Bibliographic record
Abstract
Abstract This study aimed to investigate the specificity of the Inventory of Problems (IOP) tests, specifically the IOP-29 and its memory module (IOP-M), in a high-stakes environment. The study involved 114 Italian adults who applied for the renewal or reinstatement of their driver’s license after it had been revoked due to psychiatric, cognitive, or legal issues. The IOP-29 and the IOP-M were administered alongside other tests. Data analysis revealed very few positive results for both the IOP-29 and the IOP-M, indicating high specificity in detecting a possible negative response bias. In fact, the false positive rate (or, more accurately, the presumably false positive rate) was less than 5% for each of the two IOP components, meaning that the specificity for the standard cutoff values of each IOP component (i.e., IOP-29 ≥ 0.50 and IOP-M ≤ 29) was above 0.95. Taken together, these results contribute to the growing body of research supporting the use of the IOP-29 and IOP-M in applied settings where mild cognitive impairment might be present. However, further studies are needed to validate these results in populations with moderate or severe cognitive impairment.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".