Developing a person centred low secure model of care for forensic populations in NSW
Bibliographic record
Abstract
Systems to support individualised care and treatment of forensic mental health patients have not been a priority in Australasia when compared to more well-developed systems in the UK and Canada. Despite the available legislative provisions in Australia, the mentally ill offender population in New South Wales (NSW) is often not supported with accommodation appropriate for the levels of restriction they need as they move towards community re-entry. A significant number of patients within the NSW forensic mental health system continue to be accommodated in unnecessarily high security facilities due to a lack of low-secure facilities and supported community placements, even though the National Disability Insurance Scheme (NDIS) and programs such as Housing and Accommodation Support Initiative (HASI+) have improved provision of this in recent years. This study thematically analyses interviews with clinical experts working in forensic mental health in NSW to conceptualise a new model of care to appropriately support people with mental illness who require low-secure forensic mental health services, and to consider how such a model could be implemented. Semi-structured interviews were conducted in two phases. The first phase involved 23 purposively selected experienced forensic mental health clinicians - primarily nurses, doctors, psychologists, social workers, occupational therapists and administrators - with interest, knowledge, and experience in forensic mental health services to gain insight into issues with the current system and relevant components of a new model for low-secure forensic care in NSW.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".