Bibliographic record
Abstract
Introduction Determine the prevalence of sexual assault in an inpatient forensic population. Objectives Find individual-biological-social factors that may be associated with sex offending. Method A cross-sectional study of patients admitted to a Canadian forensic unit. Results Sample of 23 patients (95.7% male), of whom 13% had committed a sex offense. All men, 17% had attempted a murder, 17% had committed a murder, 48% had commited an assault and 5% had commited a different type of crime. 61% of the sample were in the low socioeconomic class and 39% to the medium one. 35% were diagnosed with developmental delay and 30% with personality disorders. A person that had committed a sexual offense crime was much older, compared to the rest of the forensic population (43.33 ± 17.67 vs. 30.80 ± 11.93 years). Moreover the average stay in the hospital was almost the double for the sexual offenders compared with rest of the sample (199.67 months vs. 89.35 months). Among the sexual offenders the prevalences of the diagnoses of AXIS I were, 67% psychoses, 33% mood disorders and 67% substance abuse. However all the sexual offenders were diagnosed with a comorbidity different than those compared with those that were not sexual offenders (100% vs 20%, p=0.003) Conclusions Older population who commits a sexual offense have a longer average stay at forensic unit. Increased presence of comorbidities, associated at main diagnosis in axis I and organic comorbidities, as well as different treatments used.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".