Clinical and Psychosocial Stress Correlates of Self‐Harm in Women: A Retrospective Cohort Study in the Forensic Mental Health Setting
Bibliographic record
Abstract
Para-suicidal behaviours and self-harm are commonly observed in mental health institutions, with women inpatients being particularly at risk. Little research has looked at characteristics of women engaging in self-harmful conduct beyond diagnostic and socio-demographic information. The current study investigated occurrences of self-harm over time in a large sample of women housed in forensic mental health units in the province of Ontario, Canada. Background and clinical information was obtained from staff ratings on the Resident Assessment Instrument-Mental Health at admission and every 3 months afterwards for an approximately 2-year time period. Latent class mixed models identified two distinct profiles, the first one (77.4% of sample) characterised by a low or intermittent use of self-harm, the second (22.6% of sample) showing a stable elevated risk profile. Women in the at-risk group tended to be younger, showed increased signs of subjective distress and greater occurrence of adverse life events in their history. Psychiatric diagnosis in itself was not a valid predictor of the stability of self-harm for this sample. These findings highlight the importance of addressing both clinical and stress-related distal vulnerability factors in the background of institutionalised women who engage in self-harm on a stable basis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".