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Record W2114594181 · doi:10.1177/0013916508330392

The Effect of Environmental Design on Reducing Nursing Errors and Increasing Efficiency in Acute Care Settings

2009· article· en· W2114594181 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironment and Behavior · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsAcute careFocus groupNursingBurnoutPatient safetyHuman factors and ergonomicsAffect (linguistics)MedicineNursing careApplied psychologyPsychologyMedical emergencyPoison controlHealth careBusiness

Abstract

fetched live from OpenAlex

Physical environment is an important component in the acute care setting that can affect nursing and medication accuracies, as any inadequacy in physical environment would contribute to staff fatigue, stress, and burnout and result in errors. The article discusses a study conducted involving an extensive review and analysis of the literature on this topic and focus groups with various categories of staff members at three hospitals. The review demonstrates that the following environmental variables can contribute to errors in acute care settings: noise levels, ergonomics/furniture/equipment, lighting, and design/layout. Focus groups address the role of the physical environment on medication ordering, storage, delivery, dispensation, preparation, administration, and possible design responses to reduce errors. Integrating the major issues identified and the key findings from the focus groups, four design-related principles are recommended: balance between patient accessibility and reduction of disruptions, automation, minimize staff fatigue, and promoting a culture of safety.

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.

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)0.000
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 score0.479
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.333
Teacher spread0.316 · 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