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Record W2957304985 · doi:10.1097/xeb.0000000000000178

Getting guidelines into practice

2019· article· en· W2957304985 on OpenAlexaffabout
Teresa Moreno‐Casbas, Esther González‐María, Laura Albornos-Muñóz, Doris Grinspun

Bibliographic record

VenueInternational Journal of Evidence-Based Healthcare · 2019
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsRegistered Nurses' Association of Ontario
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: The Spanish Best Practice Guidelines (BPG) Implementation Project is part of the Best Practice Spotlight Organizations international program, coordinated by the Registered Nurses' Association of Ontario (RNAO). AIMS: To influence the uptake of nursing BPG across healthcare organizations, to enable practice excellence and positive client outcomes. METHODS: After translating the RNAO's BPG into Spanish, the Host Organization published a formal call for proposals to select healthcare settings in Spain to implement the RNAO's BPG and evaluate the results. The approach is nursing-led and multidisciplinary; context specific; and involving a wide range of stakeholders. The implementation of BPG Toolkit guides the process: cascade training, selection of recommendations to be implemented, 3 years of planned implementation activities, monitoring of process and outcome results for patients discharged 60 days every year. The Host Organization supports healthcare settings selected. RESULTS/DISCUSSION: The first call was launched in 2012. Eight healthcare settings (11 sites), serving 1.3 million people, were selected (hospitals and primary healthcare centers). They chose 10 BPG, according to their needs. In 2015 and 2018, 16 more healthcare settings have joined the program with a total of 263 sites. And in 2019, three complete regions will join the program as a regional host. Currently, more than 3200 nurses and 40 other healthcare professionals have been trained, evidence-based protocols have been developed or updated, patient education has been promoted, and international Best Practice Spotlight Organizations indicators have been evaluated in an electronic platform. CONCLUSION: The results obtained acknowledge that the RNAO implementation method could be replicated with success internationally. The strategies based on local context have worked and we have consolidated a network that shares knowledge and strategies and promotes evidence-based culture among Spanish healthcare settings and evidence-based care to 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.063
metaresearch head score (Gemma)0.190
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.063
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.190
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0030.004
Scholarly communication0.0110.012
Open science0.0040.011
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0420.028

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 head0.579
GPT teacher head0.618
Teacher spread0.038 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations10
Published2019
Admission routes2
Has abstractyes

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