Challenging behaviour in the elderly—monitoring violent incidents
Bibliographic record
Abstract
OBJECTIVE: To explore the frequency and nature of violent incidents in psychogeriatric wards and nursing homes in terms of type and severity of incidents, what provoked the incidents, and what kind of measure was needed to stop the aggression. MATERIAL AND METHODS: Aggressive behaviour of the study group was monitored using the Staff Observation Aggression Scale-Revised (SOAS-R( in two Norwegian nursing homes and two geriatric psychiatric wards for a period of three months. Severity of incidents were monitored with the built-in severity scoring system in SOAS-R. RESULTS: During the study period 32 out of the 82 patients were reported to be violent. The majority of the incidents were generated by a minority of the patients. Physical injury to the staff as a consequence of the aggression was extremely rare. Situations where the client was denied something were the most provocative ones and a substantial number of incidents occurred at bath/shower times. Talking to the patient was the most frequent measure used to stop the aggression, but more intrusive measures were also used. CONCLUSIONS: A substantial proportion of the incidents were associated with personal care tasks, suggesting a crucial role for communication difficulties and a focus for staff training. We suggest that personal care situations should be added to the variable list in future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".