Violence Risk Assessment in Forensic Nurses’ Clinical Practice: A Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: The legislation of Swedish forensic psychiatric care states that the risk of further violence must be assessed before a patient is granted release from a forensic psychiatric hospital. The aim of the study was to describe the experiences of forensic nurses with in-patient risk assessment processes, and their implication for daily clinical forensic praxis.METHOD: Semi-structured interviews with staff who were involved in the patients risk assessment process. The interview texts were analyzed using qualitative latent content analysis.DISCUSSION: The forensic nursing staff has to deal with many contradictory realities. The description was about being able to balance between supporting their work with an EBP approach of risk assessment while trying to establish interpersonal relationships and to allow for positive meetings with the patient. The study indicated that staff used a multiple sources of knowledge in order to make credible and accurate risk assessments.CONCLUSIONS: If the risk assessment process are to be used in a legally secure manner, the staff must receive regular support from team leadership that can provide both guidance and training. Based on a holistic approach, the link between the instinct of staff and their work with structured risk assessment must be founded on routines and solid platforms.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".