Relationship Between Assault Frequency and Length of Hospitalization in Older Patients With Dementia: <i>Determining the Maximum Benefit of Inpatient Treatment</i>
Bibliographic record
Abstract
In this quantitative study, the author examined the relationship between duration of hospitalization and frequency of assaultive behavior in 42 older long-term patients with dementia in a Canadian psychiatric hospital. The study instrument used for data collection was existing incident reporting forms routinely completed in Canadian regional psychiatric hospitals. A secondary analysis was conducted using data previously collected on a regular basis by the psychiatric hospital serving as the study site. A significant negative correlation was found between the number of assaults committed and the number of months spent in the hospital, with significantly fewer assaults occurring in the second year of hospitalization compared with the first year. Male patients were observed to be significantly more assaultive than female patients. Findings suggest that the maximum benefit for patients hospitalized for assaultive behavior is obtained during the first 2 years of inpatient treatment and that patients within this population who are no longer assaultive may be more appropriately cared for in nursing homes. Based on these findings, resources should be allocated to assist with the transition of formerly assaultive patients with dementia from a psychiatric hospital to a nursing home. This scenario forecasts the development of a challenging new role for nurses.
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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 | 0.009 |
| 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 | 0.000 |
| 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 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".