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Record W1960395035 · doi:10.1111/wvn.12026

The Nurse as Bricoleur in Falls Prevention: Learning from a Case Study of the Implementation of Fall Prevention Best Practices

2014· article· en· W1960395035 on OpenAlexaff
Helen Kirkpatrick, Sheryl Boblin, Sandra Ireland, Kim Robertson

Bibliographic record

VenueWorldviews on Evidence-Based Nursing · 2014
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsSt Joseph's Health CareMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsNursingFall preventionMedicineQualitative researchAction (physics)BricolagePsychologySuicide preventionPoison controlEnvironmental healthSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Falls prevention in "real-life" clinical practice is a complex undertaking. Nurses play an active and essential role in falls prevention. AIM: This discussion paper presents a picture of the nurse as a bricoleur in falls prevention, requiring knowledge in many areas and the ability to perform multiple diverse tasks. METHODS: Building on a qualitative case study with nurses at various levels in three acute care facilities, this paper posits that the concept of nurse as bricoleur has the potential to broaden our understanding of the complexity of falls prevention. FINDINGS: The nurse as bricoleur within the Promoting Action Research in Health Services framework as the provider of person- or patient-centered evidence-based care is conceptualized. Within this framework, the nurse uses his or her professional knowledge or clinical experience while considering research, local data, and information, and the patient's experience and preferences to provide this care, the bricolage. Each of these areas is discussed as well as the impact on the nurse when a fall does occur. LINKING EVIDENCE TO ACTION: Recognizing this complexity of the nurses' world has important implications for both service delivery and education, including preparation of students, and the implementation of new organizational initiatives and supports for nurses when falls do occur despite the best efforts of all involved.

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)0.004
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 score0.471
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.115
GPT teacher head0.477
Teacher spread0.362 · 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

Citations18
Published2014
Admission routes1
Has abstractyes

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