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Record W2747874218 · doi:10.5430/jnep.v8n1p9

Evaluation of evidence-based nursing education of hospital acquired pressure injury prevention in clinical practice: An integrative review

2017· article· en· W2747874218 on OpenAlexvenueno aff
Michelle Henry, Cynthia Foronda

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsnot available
FundersSchool of Nursing, Johns Hopkins UniversityJohns Hopkins University
KeywordsPressure injuryNursingPsychological interventionMedicineCritical appraisalInclusion (mineral)Best practiceQuality (philosophy)PsychologyAlternative medicine

Abstract

fetched live from OpenAlex

Background and objective: There are 2.5 million Americans affected by hospital-acquired pressure injuries (HAPI) annually. The objective of this study was to review and synthesize the evidence on competency-based education of interventions to prevent HAPIs and to improve the knowledge-base of nursing to guide quality and safety initiatives for patients.Methods: A total of 30 articles met the inclusion and exclusion criteria. They were reviewed, and the selected articles focused into three main areas: nurse knowledge and education, HAPI prevention improvement, and competency. These articles were examined to comprise a review on the studies that provided the most relevant synchronized data concerning pressure ulcers and competency-based education.Results: Two themes developed during the literature search and analysis of the selected articles. The first theme focused on nurse education programs for the prevention and identification of HAPI, and the second was the need for nurse knowledge and competency in the prevention of HAPI.Conclusions: Appraisal of the literature showed that various HAPI education programs have improved nurses’ knowledge and competency, and decreased HAPI occurrences. Future research should focus on identifying and reinforcing standardized professional competency-based education to create a culture of success, and ensure consistently high quality care and safe outcomes for patients.

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)0.026
metaresearch head score (Gemma)0.052
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.004
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 head0.393
GPT teacher head0.662
Teacher spread0.268 · 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 designOther design
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

Citations3
Published2017
Admission routes1
Has abstractyes

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