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Record W3011337136 · doi:10.1097/rnj.0000000000000259

Examining Fall Risk Assessment in Geriatric Rehabilitation Settings Using Translational Research

2020· article· en· W3011337136 on OpenAlexaffabout
Catherine A. Rivers, Haley Roher, Bruce A. Boissonault, Christopher Klinger, Raza Mirza, Richard Foty

Bibliographic record

VenueRehabilitation Nursing · 2020
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsInstitute for Work & HealthPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsCoachingFall preventionRehabilitationAccountabilityFocus groupMedicineNursingRisk assessmentHuman factors and ergonomicsPsychologyPoison controlPhysical therapyMedical emergencyComputer science

Abstract

fetched live from OpenAlex

PURPOSE: The objective of this study was to identify gaps in and to improve the falls prevention strategy (FPS) of an inpatient rehabilitation facility (IRF) in Toronto, Canada. DESIGN: A modified version of the Stanford Biodesign Methodology was used. METHODS: Chart reviews, a focus group (n = 8), and semistructured interviews (n = 8) were conducted to evaluate the FPS. FINDINGS: Admission Functional Independence Measure score, age, and gender significantly correlated with risk for a fall. The tool used at this IRF was not effectively capturing patients who were at high risk for falls. All healthcare providers interviewed were knowledgeable of fall risks; however, a patient's fall risk status was rarely discussed as a team. CONCLUSIONS: The findings informed recommendations to improve the overall FPS at this IRF. CLINICAL RELEVANCE: Staff may require more coaching for implementing preventative measures/ensuring accountability and evaluating whether current strategies work. These insights can guide improvement initiatives at similar facilities elsewhere.

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)0.006
metaresearch head score (Gemma)0.004
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 score0.077
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.093
GPT teacher head0.464
Teacher spread0.371 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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