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Record W2003490744 · doi:10.7739/jkafn.2012.19.1.087

Evaluation of Applications of Adaptation of the Evidence-Based Nursing Practice Guidelines Patients with Acute Stroke

2012· article· en· W2003490744 on OpenAlexaboutno aff
So-Lee Song, Myoung-Sook Cho, Jihyun Kim, Yun-Kyang Han, Hye-Min Yan

Bibliographic record

VenueJournal of Korean Academy of Fundamentals of Nursing · 2012
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGuidelineStroke (engine)Acute strokeMedical recordPhysical therapyEmergency medicineNursingEmergency departmentSurgery

Abstract

fetched live from OpenAlex

Purpose: This study was done to evaluate nursing guidelines for patients with acute stroke, developed by adapting the guidelines of Registered Nurses Association of Ontario, Canada to clinical settings on a large scale and evaluating the effectiveness as a research study. Method: The general characteristics of the 319 patients and the effectiveness of guideline application were evaluated in terms of structure, process, and outcome using questionnaires on the guidelines application with reference to the medical records of patients with acute stroke hospitalized on a ward of the stroke center of S General Hospital in Seoul. Results: Structures as a guidance system for assessment were consistent with the recommendations. With respect to the process of the guidelines, for items on nursing assessment, improved performance was found to be statistically significant. For outcomes of the guidelines, complications occurred in 8 patients (5.3%) prior to application of the guidelines and 11 patients (6.5%) after application of the guidelines, but this result was not statistically significant (p=.841). Conclusion: The results of the study indicate that for the effectiveness of the guidelines, accessibility to the guidelines and effectiveness of quality improvement need to be evaluated, in addition to complications of a stroke.

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)0.007
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.440
GPT teacher head0.553
Teacher spread0.113 · 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 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

Citations2
Published2012
Admission routes1
Has abstractyes

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