Specificity of the MMPI–2 Fake Bad Scale as a Marker for Personal Injury Malingering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Psychologists who evaluate patients in medicolegal contexts should utilize objective assessment data with empirically established sensitivity and specificity for identifying negative response bias. The purpose of this study was to investigate the specificity of the Fake Bad Scale for identifying negative response bias in personal injury claimants. The cutoff scores proposed by Lees-Haley and colleagues were applied a federal prison, medical outpatients, and patients from to inmate volunteers from substance abuse unit. Half of the inmates were given instructions to malinger psychopathology to affect the adjudication process, and the remaining inmates and all of the hospital patients were given standard instructions. The original cutoff scores correctly identified the majority of inmates instructed to malinger psychopathology, but these scores resulted in unacceptably high rates of false positive classifications. The revised cutoff scores resulted in fewer false positives, i.e., 8%-24%.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it