Characteristics of referrals and admissions to a medium secure ASD unit
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present preliminary data on a cohort of patients referred to a specialist forensic medium-secure autism spectrum disorder (ASD) service during its first two years of opening and to identify variables associated with admission to the service. Design/methodology/approach – Data on all referrals to the service (n=40) was obtained from clinical files on demographics, offending history, psychiatric history and levels of therapeutic engagement. The sample was divided into two groups: referred and admitted (n=23) and referred and not admitted (n=17). Statistical analysis compared the two groups on all variables. Findings – Totally, 94 per cent of all individuals assessed had a diagnosis of autism, however, structured diagnostic tools for ASD were used in a small minority of cases. About half the sample had a learning disability, almost four-fifths had at least one additional mental disorder and almost three-quarters had a history of prior supervision failure or non-compliance with treatment. The sample had a wide range of previous offences. No significant differences were found between the groups on any of the variables included in the study. Research limitations/implications – The present study presents a starting point to follow up in terms of response to treatment and characteristics associated with treatment outcome. Practical implications – The sample had a wide range of clinical and risk-related needs. Both groups shared many similarities. Originality/value – This highlights the need for comprehensive assessment looking at risk-related needs so that individuals are referred to an optimal treatment pathway.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".