Is There a Need for Elderly Forensic Psychiatric Services?
Bibliographic record
Abstract
The population of the elderly in most developed nations is on the increase. Furthermore, the prevalence of mental disorder amongst elderly offenders is high. The true extent of 'elderly' crime is unknown because much of it goes undetected and unreported. This leads to a failure to detect mental illness in such offenders. Court diversion schemes may improve recognition of mental illness but these schemes usually tend to deal with the more severe crimes. This may result in an overestimation of the amount of serious crime committed by the elderly and a failure to detect mental illness amongst those who commit less serious crimes. Efforts to service this hidden morbidity call for multi-agency collaboration. Improved detection and reporting of crimes is essential if mental health difficulties in the elderly are not to go unnoticed. The needs of elderly mentally-disordered offenders are complex and fall within the expertise of old age and forensic psychiatry, without being adequately met by either one. Therefore, consideration should be given to the development of a tertiary specialist forensic old-age psychiatry service.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".