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Record W4295086786 · doi:10.11124/jbies-21-00313

Knowledge, skills, attitudes, beliefs, and implementation of evidence-based practice among nurses in low- and middle-income countries: a scoping review protocol

2022· review· en· W4295086786 on OpenAlexaff
Stephen Adombire, Martine Puts, Lisa M. Puchalski Ritchie, Mary Ani–Amponsah, Lisa Cranley

Bibliographic record

VenueJBI Evidence Synthesis · 2022
Typereview
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity Health NetworkSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsCINAHLGrey literatureMEDLINEEvidence-based practiceHealth careMedicineEmpirical evidenceInclusion (mineral)NursingMedical educationPsychologyAlternative medicinePsychological interventionPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this scoping review is to identify and map the evidence reporting the knowledge, skills, attitudes, beliefs, and implementation of evidence-based practice among nurses in low- and middle-income countries. INTRODUCTION: Evidence-based practice aims to improve health care quality, safety, and health system efficiency. Numerous research studies have explored nurses' engagement in evidence-based practice in high-income countries. Developing nations have recently joined the evidence-based practice movement, and primary research about nurses' engagement with it are emerging. INCLUSION CRITERIA: The scoping review will include primary studies (quantitative, qualitative, and mixed methods) and gray literature addressing knowledge, skills, attitudes, beliefs, and implementation of evidence-based practice among nurses. Participants will include registered nurses working in low- and middle-income countries. Studies conducted in all health care settings, including acute and community settings, in low- and middle-income countries will be included. METHODS: We will search MEDLINE, Embase, CINAHL, Scopus, ERIC, JBI Evidence-based Practice Database, Cochrane Library, LILACS, and AIM. Gray literature will be sourced from ProQuest Dissertations and Theses Global and Google Scholar for primary studies. Studies published in the English language will be included, with no limit on publication date. Titles, abstracts, and full-text articles will be assessed against the inclusion criteria by 2 independent reviewers. The extracted data will be analyzed quantitatively using frequencies and counts. Textual data from qualitative studies will be analyzed using descriptive content analysis. Results of the data analysis will be presented using graphs, tables, and a narrative format. SCOPING REVIEW REGISTRATION: Open Science Framework ( https://osf.io/hau5y ).

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)0.136
metaresearch head score (Gemma)0.117
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.136
Threshold uncertainty score0.718

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1360.117
Meta-epidemiology (narrow)0.0050.007
Meta-epidemiology (broad)0.0150.014
Bibliometrics0.0260.018
Science and technology studies0.0080.006
Scholarly communication0.0090.010
Open science0.0070.009
Research integrity0.0120.007
Insufficient payload (model declined to judge)0.0430.011

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 head0.194
GPT teacher head0.592
Teacher spread0.397 · 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
GenreProtocol

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
Published2022
Admission routes1
Has abstractyes

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