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Record W3008847252 · doi:10.1136/bmjoq-2019-000703

Improving urinary catheterisation practices in a rural hospital in Ontario

2020· article· en· W3008847252 on OpenAlexfundaboutno aff
Mohamed Gazarin, Jennifer Ingram-Crooks, Fatima Hafizi, Lynn Hall, Kirsti Weekes, Cindy Casselman, Sean Burnett, Mikyla Lennard, Amanda Pinches, Darren Tse

Bibliographic record

VenueBMJ Open Quality · 2020
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsnot available
FundersOntario Ministry of Health and Long-Term Care
KeywordsPDCAMedicinePsychological interventionUrinary systemCatheterAuditIntensive care medicineQuality managementEmergency medicineNursingMedical emergencyOperations managementSurgeryInternal medicineManagement systemBusiness

Abstract

fetched live from OpenAlex

INTRODUCTION: A urinary catheter constitutes a one-point patient restraint, can induce deconditioning and may lead to patient mortality. An audit performed at Winchester District Memorial Hospital revealed that 20% of patients had a urinary catheter, of whom 31% did not meet the criteria for catheterisation. The main objective of this study was to use the Influencer Change Model and the Choosing Wisely Canada toolkit to create a bundle of interventions that would reduce the unnecessary use of urinary catheters in hospitalised patients. METHODS: In a rural teaching hospital, a time-series quasi-experiment was employed to decrease inappropriate use of urinary catheters. Both the Choosing Wisely Canada toolkit for appropriate use of urinary catheters and the Influencer change management approach were used to create effective interventions. RESULTS: This study revealed that there was no improvement in appropriate urinary catheter use during Plan-Do-Study-Act (PDSA) cycle 1. There was gradual improvement during PDSA cycle 2, with the percentage of inappropriate urinary catheter use dropping from an initial 31% before any interventions to less than 5% by the end of this study. DISCUSSION/CONCLUSION: This study aimed to reduce the inappropriate use of urinary catheters in a rural hospital with limited resources. The findings indicate that by using a change model, such as the Influencer Change Model, it is possible to promote better patient care through empowering healthcare staff to implement accepted protocols more stringently and thereby to decrease the inappropriate use of urinary catheters to 0%.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.168
GPT teacher head0.435
Teacher spread0.267 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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