Self-harming behaviors among forensic psychiatric patients living with intellectual disability
Bibliographic record
Abstract
BACKGROUND: Individuals with intellectual disabilities (ID) are frequently involved in the criminal justice system, and many subsequently enter the forensic psychiatric system. While individuals with ID in forensic psychiatric settings are known to have a high burden of engaging in self-harming behaviors, limited studies have explored self-harming behaviors among them. AIM: To determine the prevalence of ID and explore the burden of self-harming behaviors and the associated factors among forensic psychiatric patients with ID. METHODS: This retrospective study utilized data on 155 patients diagnosed with ID under the Ontario Review Board during the reporting year 2014 to 2015. The primary outcome variable was engagement in physical self-harming behaviors. Factors associated with self-harm were identified using logistic regression analysis, performed with STATA-17. RESULTS: The prevalence of ID among forensic psychiatric patients in Ontario was found to be 13.1%. Of these patients, 43.2% had their Full-Scale Intelligence Quotient (FSIQ) score included in the report used for the database. The prevalence of self-harming behaviors among patients with ID was 9.7%. The likelihood of self-harm was significantly lower in males (adjusted odds ratio [aOR] = 0.02, 95% confidence interval [CI] = 0.002-0.47, p-value = 0.013) and significantly higher in those with a previous history of self-harm (aOR = 28.21, 95% CI = 1.61-494.66, p-value = 0.022). CONCLUSION: This study found a high burden of both ID and self-harm, especially among females and those with prior history of self-harming. These findings highlight the need for relevant resources, targeted interventions, and specialized programs to mitigate self-harm in this vulnerable population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".