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Record W2268055926 · doi:10.3928/00989134-20070401-03

Relationship Between Assault Frequency and Length of Hospitalization in Older Patients With Dementia: <i>Determining the Maximum Benefit of Inpatient Treatment</i>

2007· article· en· W2268055926 on OpenAlexaffabout
Troy Savage

Bibliographic record

VenueJournal of Gerontological Nursing · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsDementiaMedicineInjury preventionPsychiatric hospitalPsychiatrySuicide preventionPopulationOccupational safety and healthPoison controlHuman factors and ergonomicsNursing homesEmergency medicineNursingDisease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.313
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2007
Admission routes2
Has abstractyes

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