Who’s going to leave? An examination of absconding events by forensic inpatients in a psychiatric hospital
Bibliographic record
Abstract
Absconding is a potentially risky event that has wide reaching consequences both for the institution and greater community; however, few studies have examined the characteristics of clients who abscond, their motivations, and details about their absconding event, especially within a forensic context. The purpose of this research was to determine if risk factors could be identified that might predict absconding behavior. A retrospective chart review was conducted of all reported absconding events between 1 January 2012 and 31 August 2015 by clients on forensic units in a public psychiatric hospital in Ontario, Canada. In addition, these clients were matched with a comparison group. Categories of motivations for absconding including goal-directed, frustration/boredom, symptomatic/disorganized, and impulsive/opportunistic were identified. The best indicator of a client’s risk for absconding was having experienced a stressful, significant event in the two weeks prior to the absconding event. Additionally, total scores on the HCR-20 and the presence of a co-occurring substance use disorder differentiated the absconders from the comparison group. This research contributes to our knowledge base regarding absconding events by forensic psychiatric patients and highlights specific targets for clinical staff in assessing risk for absconding and managing privileges leading to more effective care planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".