MétaCan
Menu
Back to cohort
Record W3203193860

[Implementation of the "Best Practice Spotlight Organization" Program at the Virgen de las Nieves University Hospital.]

2021· article· en· W3203193860 on OpenAlexaboutno aff
Ma Dolores Quiñoz Gallardo, Elena Gonzalo Jiménez, Sergio Barrientos‐Trigo, Ana María Pórcel‐Gálvez

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Process managementHealth careAdaptation (eye)Identification (biology)Process (computing)Best practicePsychological interventionNursingComputer scienceKnowledge managementBusinessMedicinePsychologyPolitical scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

) program in the cohort (2015-2017) to implement three guidelines for Nurses Association of Canada Ontario (RNAO) clinical practice of care. The methodology used was the model called "knowledge for action" and the actions developed for each of the phases of the action cycle for applying knowledge to practice were described: 1) identification of the problem, 2) adaptation to the local context, 3) evaluation of facilitators and barriers, 4) adaptation and implementation of interventions, 5) monitoring and evaluation of results and 6) sustainability. This work adds to the set of studies that address the improvement and maintenance of evidence-based practice programs in nursing, and in health services in general. It shows the application of a framework for the implementation of clinical practice guidelines for care in a specific health environment for its replication in other different health settings. It has been shown that it is essential to dedicate efforts to planning the implementation of this type of programs, taking into account the context in which they are developed, the specific characteristics of the population being served, identifying the different barriers and facilitators that may affect during the course of the program. process and defining actions to make the changes in practice sustainable.

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)0.000
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 score0.360
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.262
Teacher spread0.253 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicNursing Diagnosis and DocumentationFrench-language works237,207