MétaCan
Menu
Back to cohort
Record W2070819055 · doi:10.1002/nur.20185

Description of an advanced practice nursing consultative model to reduce restrictive siderail use in nursing homes

2007· article· en· W2070819055 on OpenAlexaff
Laura M. Wagner, Elizabeth Capezuti, Barbara L. Brush, Marie Boltz, Susan M. Renz, Karen Amann Talerico

Bibliographic record

VenueResearch in Nursing & Health · 2007
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsBaycrest Hospital
FundersNovartis FoundationHartford Foundation for Public GivingUniversity of PennsylvaniaJohn A. Hartford Foundation
KeywordsMedicineNursingNursing homesPsychological interventionMEDLINENursing practiceGerontology

Abstract

fetched live from OpenAlex

Researchers have demonstrated that the use of physical restraints in nursing homes can be reduced, particularly where advanced practice nurses (APNs) are utilized. We examined the link between APN practice, siderail reduction, and the costs of siderail alternatives in 273 residents in four Philadelphia nursing homes. The majority of participants were cognitively and physically impaired with multiple co-morbidities. APNs recommended a total of 1,275 siderail-alternative interventions aimed at reducing fall risk. The median cost of siderail alternatives to prevent falls per resident was $135. Residents with a fall history experienced a significantly higher cost of recommendation compared to non-fallers. Findings suggest that an APN consultation model can effectively be implemented through comprehensive, individualized assessment without incurring substantial costs to the nursing home.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.270
GPT teacher head0.598
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations35
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueResearch in Nursing & HealthSame topicHealthcare Decision-Making and RestraintsFrench-language works237,207