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Nurses' Perceptions of Safety Culture in Long‐Term Care Settings

2009· article· en· W2009991464 on OpenAlexaffabout
Laura M. Wagner, Elizabeth Capezuti, Julie Rice

Bibliographic record

VenueJournal of Nursing Scholarship · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsHospital for Sick ChildrenBaycrest Hospital
FundersSigma Theta Tau InternationalAmerican Nurses Foundation
KeywordsSafety cultureNursingPatient safetyOrganizational culturePerceptionGovernment (linguistics)Psychological interventionMedicinePsychologyHealth carePublic relationsManagement

Abstract

fetched live from OpenAlex

PURPOSE: To describe perceptions of workplace safety culture among nurses employed in long-term care (LTC) settings. DESIGN: A cross-sectional survey. Respondents were licensed nurses (N=550) with membership in gerontological nursing professional organizations in the United States (n=296), Canada (n=251), and other (n=3). METHODS: An anonymous, self-administered, mail-in questionnaire, which included the Hospital Survey on Patient Safety Culture as well as questions about individual and institutional characteristics. The survey included key aspects of safety culture, such as work setting, supervisor support, communication about errors, and frequency of events reported. FINDINGS: Nurse-managers reported significantly more positive safety culture perceptions compared with licensed staff nurses. Additionally, licensed nurses employed in government-run facilities had significantly less positive safety culture perceptions compared with those working in nonprofit organizations. CONCLUSIONS: Interventions designed to improve safety culture in LTC settings should be focused on the concerns of licensed staff nurses and the improvement of communication between these nurses and their managers. CLINICAL RELEVANCE: Enhancing safety culture in long-term care settings may facilitate improvements in resident safety. Assessment of workplace safety culture is the first step in identifying barriers that nurses face to provide safe resident care.

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 score0.255
Threshold uncertainty score0.762

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.052
GPT teacher head0.459
Teacher spread0.407 · 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

Citations74
Published2009
Admission routes2
Has abstractyes

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