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Record W1938376785 · doi:10.1111/ijn.12160

Patient perception of contemporary nurse attire: A pilot study

2013· article· en· W1938376785 on OpenAlexafffundabout
Caroline Porr, Doreen Dawe, Nicole Lewis, Robert J. Meadus, Nicole Snow, Paula Didham

Bibliographic record

VenueInternational Journal of Nursing Practice · 2013
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsGovernment of Newfoundland and LabradorNewfoundland and Labrador Centre for Applied Health ResearchMemorial University of Newfoundland
FundersEastern HealthHealth Care Foundation
KeywordsNursingPerceptionPsychologyMedicine

Abstract

fetched live from OpenAlex

Patients have expressed difficulty accurately distinguishing registered nurses (RNs) from other hospital personnel because standardized uniforms are no longer worn by RNs. According to American studies, such complaints are widespread; moreover, patients' perceptions of nurse caring and competence and of other traits associated with nurses' professional image have been negatively affected by casual, non-conventional attire. As there are no published Canadian studies, we conducted a pilot study to examine patient perception of the nurse uniform. Adult patients viewed photographs of the same RN dressed in eight different uniforms and rated each uniform according to 10 traits associated with nurses' professional image. The white pantsuit scored higher for professionalism than uniforms with small print, bold print, or solid colour, and most patients preferred that the RN dress in white. Our preliminary findings suggest that RN attire warrants further investigation, and we are planning a large-scale, fully powered study to inform patient-driven change to existing uniform policies.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.073
GPT teacher head0.421
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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2013
Admission routes3
Has abstractyes

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