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Record W4206444969 · doi:10.1093/ageing/afab256

Barriers and facilitators to nursing delirium screening in older emergency patients: a qualitative study using the theoretical domains framework

2021· article· en· W4206444969 on OpenAlexafffund
Debra Eagles, Warren J. Cheung, Tanja Avlijas, Krishan Yadav, Robert Ohle, Monica Taljaard, Frank Molnar, Ian G. Stiell

Bibliographic record

VenueAge and Ageing · 2021
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsNOSM UniversityScience NorthOttawa HospitalBruyèreUniversity of Ottawa
FundersOttawa Hospital Research Institute
KeywordsDeliriumMedicineWorkloadEmergency departmentContext (archaeology)Qualitative researchNursingProtocol (science)PerceptionPsychologyPsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: delirium is common in older emergency department (ED) patients, but vastly under-recognised, in part due to lack of standardised screening processes. Understanding local context and barriers to delirium screening are integral for successful implementation of a delirium screening protocol. OBJECTIVES: we sought to identify barriers and facilitators to delirium screening by nurses in older ED patients. METHODS: we conducted 15 semi-structured, face-to-face interviews based on the Theoretical Domains Framework with bedside nurses, nurse educators and managers at two academic EDs in 2017. Two research assistants independently coded transcripts. Relevant domains and themes were identified. RESULTS: a total of 717 utterances were coded into 14 domains. Three dominant themes emerged: (i) lack of clinical prioritisation because of competing demands, lack of time and heavy workload; (ii) discordance between perceived capabilities and knowledge and (iii) hospital culture. CONCLUSION: this qualitative study explored nursing barriers and facilitators to delirium screening in older ED patients. We found that delirium was recognised as an important clinical problem; however, it was not clinically prioritised; there was a false self-perception of knowledge and ability to recognise delirium and hospital culture was a strong influencer of behaviour. Successful adoption of a delirium screening protocol will only be realised if these issues are addressed.

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)0.000
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.349
Teacher spread0.331 · 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.

Study designQualitative
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

Citations71
Published2021
Admission routes2
Has abstractyes

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