The impact of structured decision making on absconding by forensic psychiatric patients: results from an A-B design study
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated absconding from forensic hospitals and there are no published studies of interventions aimed at reducing these incidents in forensic settings. We present a study of the impact of a new policy using structured professional judgment and an interdisciplinary team-based approach to granting privileges to forensic patients. We assess the impact of this policy on the rate and type of absconding from a metropolitan forensic facility. METHODS: Following concern about the rate of absconding at our hospital, a new policy was implemented to guide the process of granting hospital grounds and community access privileges. Employing an A-B design, we investigated the rate, characteristics, and motivations of absconding events in the 18 months prior to, and 18 months following, implementation of this policy to assess its effectiveness. RESULTS: Eighty-six patients were responsible for 188 incidents of absconding during the 42-month study window. The rate of absconding decreased progressively from 17.8% of all patients at risk prior to implementation of the new policy, to 13.8% during implementation, and further to 12.0% following implementation. There was a differential impact of the policy on absconding events, in that the greatest reduction was witnessed in absconsions occurring from unaccompanied passes; this was offset, to some extent, by an increase in absconding occurring from within hospital units or from staff accompanied outings. Seven of the absconding events included incidents of minor violence, and two included the commission of other illegal behaviors. The most common reported motive for absconding across the time periods studied was a sense of boredom or frustration. Discharge rate from hospital was 22.9% prior to the implementation of the policy to 22.7% after its introduction, indicating no change in the rate of patients' eventual community reintegration. CONCLUSIONS: A structured and team-based approach to decision making regarding hospital grounds and community access privileges appeared to reduce the overall rate of absconding without slowing community reintegration of forensic patients.
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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 | 0.039 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".