Characteristics and motivations of absconders from forensic mental health services: a case-control study
Bibliographic record
Abstract
BACKGROUND: Absconding from hospital is a significant health and security issue within psychiatric facilities that can have considerable adverse effects on patients, their family members and care providers, as well as the wider community. Several studies have documented correlates associated with absconding events among general psychiatric samples; however, few studies have examined this phenomenon within samples of forensic patients where the perception of threat to public safety in the event of an unauthorized absence from hospital is often higher. METHODS: We investigate the frequency, timing, and determinants of absconding events among a sample of forensic psychiatric patients over a 24-month period, and compare patients who abscond to a control group matched along several sociodemographic and clinical dimensions. We explore, in a qualitative manner, patients' motives for absconding. RESULTS: Fifty-seven patients were responsible for 102 incidents of absconding during the two year study window. Forensic patients who absconded from hospital were more likely to have a history of absconding attempts, a diagnosed substance use disorder, as well as score higher on a structured professional violence risk assessment measure. Only one of the absconding events identified included an incident of minor violence, and very few included the commission of other illegal behaviors (with the exception of substance use). The most common reported motive for absconding was a sense of boredom or frustration. CONCLUSIONS: Using an inclusive definition of absconding, we found that absconding events were generally of brief duration, and that no member of the public was harmed by patients who absconded. Findings surrounding the motivations of absconders suggest that improvements in therapeutic communication between patients and clinical teams could help to reduce the occurrence of absconding events.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".