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Bedrail Use in English and Welsh Hospitals

2009· article· en· W1873748173 on OpenAlexaboutno aff
Frances Healey, Alexandra Cronberg, David Oliver

Bibliographic record

VenueJournal of the American Geriatrics Society · 2009
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineConfusionLogistic regressionWelshObservational studyQuarter (Canadian coin)Acute careFamily medicineEmergency medicineNursingHealth care

Abstract

fetched live from OpenAlex

OBJECTIVES: To explore rates of bedrail use, nurses' rationale, and factors related to bedrail use. DESIGN: An overnight observational study of patient and equipment characteristics related to bedrail use, analyzed using a logistic regression model. SETTING: A stratified random sample of seven organizations, drawn from 167 organizations providing acute general hospital care in England and Wales during 2006. PARTICIPANTS: One thousand ninety-two inpatients on adult inpatient wards observed at night. MEASUREMENTS: Categorical data on bedrail use related to bed type, mattress type, patient age, nurses' description of patients' mobility and confusion, and nurses' rationale for bedrail use or nonuse. RESULTS: Approximately one-quarter of patients had full bedrails raised at night; prevention of falls was the nurses' main rationale. Full bedrail use was much more likely to occur in patients who nurses described as immobile and very or slightly confused. Older patients appeared no more likely to be given bedrails than younger patients after adjusting for individual patient and equipment factors. CONCLUSION: Bedrail use varied significantly between organizations and could not be explained by differences in nurses' description of patients' mobility and confusion levels, equipment, or policy.

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)0.001
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 score0.708
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.330
Teacher spread0.311 · 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

Citations24
Published2009
Admission routes1
Has abstractyes

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