Incidents in a psychiatric forensic setting: association with patient and staff characteristics.
Bibliographic record
Abstract
Patient-related incidents are of particular concern for those working with forensic psychiatric populations. Evidence suggests that personality, stress, and burnout of nursing staff are predictive of incidents. However, the exact relationship of these factors with staff-patient interactions and the incidents that occur within these interactions have not been thoroughly explored. The authors collected data on the nature of incidents on a forensic unit within a psychiatric hospital over a 1-year period, as well as data on the characteristics of 13 staff members. They found that 10% of patients were responsible for 58% of the incidents. Patients with a diagnosis of schizophrenia were disproportionately involved in incidents. The frequency of non-violent incidents varied among nursing teams to an extent greater than that expected by chance. A relationship between incidents and some staff characteristics was also found. These results highlight the need for further research into the incidents that occur in situations where patient attributes, nurse attributes, and environmental factors produce complex interactions.
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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 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".