Knowledge, skills, attitudes, beliefs, and implementation of evidence-based practice among nurses in low- and middle-income countries: a scoping review protocol
Bibliographic record
Abstract
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 ).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".