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Record W2133519996 · doi:10.1177/0193945912437382

Nursing Home Deficiency Citations for Physical Restraints and Restrictive Side Rails

2012· article· en· W2133519996 on OpenAlexaff
Laura M. Wagner, Shawna M. McDonald, Nicholas G. Castle

Bibliographic record

VenueWestern Journal of Nursing Research · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Toronto
FundersAgency for Healthcare Research and Quality
KeywordsStaffingOddsNursing homesMedicineMedicaidCertificationNursingSkilled Nursing FacilityReimbursementPercentileRegistered nurseFamily medicineHealth careLogistic regression

Abstract

fetched live from OpenAlex

This article examines whether nursing home facility-level characteristics are associated with the likelihood of receiving deficiency citations for physical restraints, including restrictive side rails. Data from the on-line survey certification of automated records were used to calculate odds ratios for facility-level characteristics associated with these deficiency citations. Repeat records from 2000 to 2007 were combined to produce longitudinal data. The results of this study show that restraint/side rail deficiency citations were negatively associated with higher staffing levels of registered nurses and licensed practical nurses (p ≤ .001) and higher Medicaid reimbursement rates (p ≤ .01). Citations were positively associated with greater nurse aide staffing (p ≤ .01) and higher quality-of-care deficiency citation percentiles (p ≤ .001). The extent of physical restraint and restrictive side rail misuse within nursing homes appears to vary according to various facility characteristics. It is less clear how internal processes within a facility bring about these observed patterns of variation.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.262
GPT teacher head0.568
Teacher spread0.306 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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