The Inventory of Problems–29 is a Cross-Culturally Valid Symptom Validity Test: Initial Validation in a Turkish Community Sample
Bibliographic record
Abstract
Abstract Because the actuarial evidence base for symptom validity tests (SVTs) is developed in a specific population, it is unclear whether their clinical utility is transferable to a population with different demographic characteristics. To address this, we report here the validation study of a recently developed free-standing SVT, the Inventory of Problems-29 (IOP-29), in a Turkish community sample. We employed a mixed design with a simulation paradigm: The Turkish IOP–29 was presented to the same participants ( N = 125; 53.6% female; age range: 19–53) three times in an online format, with instructions to respond honestly (HON), randomly (RND), and attempt to feign a psychiatric disorder (SIM) based on different vignettes. In the SIM condition, participants were presented with one of three scripts instructing them to feign either schizophrenia (SIM-SCZ), depression (SIM-DEP), or posttraumatic stress disorder (SIM-PTSD). As predicted, the Turkish IOP–29 is effective in discriminating between credible and noncredible presentations and equally sensitive to feigning of different psychiatric disorders: The standard cutoff (FDS ≥ .50) is uniformly sensitive (90.2% to 92.9%) and yields a specificity of 88%. Random responding produces FDS scores more similar to those of noncredible presentations, and the random responding score (RRS) has incremental validity in distinguishing random responding from feigned and honest responding. Our findings reveal that the classification accuracy of the IOP–29 is stable across administration languages, feigned clinical constructs, and geographic regions. Validation of the Turkish IOP–29 will be a valuable addition to the limited availability of SVTs in Turkish. We discuss limitations and future directions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".