Bibliographic record
Abstract
Purpose – The purpose of this paper is to address how context for malingering and the provision of incentives influence malingered symptom profiles of post-traumatic stress disorder (PTSD). Design/methodology/approach – A 2 (case context)×3 (incentive) factorial design was utilized. Participants (n=298) were given an incentive (positive, negative, or no incentive), randomly assigned to a criminal or civil context, and asked to provide a fake claim of child abuse with corresponding malingered symptoms of PTSD. Under these conditions, participants completed several questionnaires pertaining to symptoms of trauma and PTSD. Findings – Results indicated that negative incentives were primarily associated with lower symptom scores. Therefore, “having something to lose” may result in more constrained (and realistic) symptom reports relative to exaggeration evidenced with positive incentives. Originality/value – These results have implications for forensic settings where malingered claims of PTSD are common and incentives for such claims (e.g. having something to gain or lose) frequently exist. Previous studies have failed to address incentives (positive and negative) in relation to a crime (i.e. abuse) that can span both criminal and civil contexts.
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 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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".