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Application of Quality Improvement Framework to reduce incidents of aggressive and abusive behaviours in an inpatient acquired brain injury unit.

2017· other· en· W6908725718 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioBoard Library Catalog (Open Research Library) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionHyporeflexiaDysgeusiaProteogenomicsArticular cartilage damageTSG101

Abstract

fetched live from OpenAlex

Incidents of challenging behaviours among patients with acquired brain injury (ABI) are exceptionally common in inpatient ABI rehabilitation units. Minimizing the occurrence of these incidents is of utmost importance to safeguard patient and staff safety and well-being. The aim of the current ongoing project is to apply a Quality Improvement Framework across three provincial ABI programs in Ontario to reduce the number of challenging behaviour incidents of patients. The authors engaged frontline staff in multiple PDSA cycles aimed at discovering the precipitating factors to challenging behaviour that was occurring at the participating facilities. Information from two structured qualitative data collection methods indicated multiple precipitating factors that influence challenging behaviour, although communicating choice options was identified as the most common precipitating factor. The authors created a measurement tool and Behaviour Skills Training protocol to communicate choice options to clients. Through baseline measures results indicated that there was potential for improvement of staff behaviour. Once the training protocol was piloted and then trained across multiple staff members, client engagement increased and the topography of the challenging behaviour became less harmful. Future direction of the project involves creating a training protocol that can be delivered across all participating site effectively and efficiently.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0240.014
Science and technology studies0.0000.002
Scholarly communication0.0010.010
Open science0.0090.012
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.436
Teacher spread0.349 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

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