Psychiatric assessment in image-based sexual abuse case: a case report on imputability in personality disorder with narcissistic traits
Bibliographic record
Abstract
Objectives: Crimes committed on the Internet and social networks are rising, and the phenomenon is complex. Knowledge of context would be useful for professionals in cases that need psychiatric assessment. We report on a case of a 27-year-old young adult who is accused of image-based sexual abuse and other crimes, for whom the examining judge requested psychiatric assessment. Methods: We conducted anamnestic collection (family, physiological and pathological, psychiatric, and toxicological), direct evaluation of the examinee, assessment of acquired health records, psychodiagnostic tests (i.e. graphic tests: Machover test, Koch test), Montreal Cognitive Assessment, Minnesota Multiphasic Personality Inventory 2, Pathological Narcissism Inventory, State-Trait Anger Expression Inventory 2, Toronto Alexithymia Scale, Thematic Apperception Test. Results: The clinical forensic assessment led to a diagnosis of Unspecified Personality Disorder (predominantly narcissistic traits) according to the DSM-5 criteria. Direct assessment showed a tendency to simulate or exaggerate symptoms, confirmed by the invalidation of the MMPI-2. In addition, the psychodiagnostic test showed a tendency to aggressive behavior and difficulty in identifying and describing emotions and feelings (alexithymia). Conclusions: This case highlights the importance of being familiar with the context of the Internet and social networks, where a rising number of crimes are committed. Forensic psychiatrists will be increasingly involved in evaluating cases related to the online world, which requires a basic knowledge of its characteristics and dynamics.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".