Knowledge, skills, attitudes, beliefs, and implementation of evidence‐based practice among nurses in low‐ and middle‐income countries: A scoping review
Bibliographic record
Abstract
BACKGROUND: Over the past three decades, research studies on nurses' engagement in evidence-based practice (EBP) have been widely reported, particularly in high-income countries, with studies from these countries dominating literature reviews. As low- and middle-income countries (LMICs) continue to join the EBP movement, primary research has emerged over the past decade about nurses' engagement with EBP. AIMS: The aim of this scoping review was to identify the types and extent of published research regarding nurses' knowledge, skills, attitudes, beliefs, and implementation of EBP in LMICs. METHODS: The JBI scoping review methodology was used. Eight databases were searched up to November 2023. The review included primary studies (quantitative, qualitative, and mixed methods) that reported the knowledge, skills, attitudes, beliefs, or implementation of EBP among nurses in LMICs. Included studies focused on registered nurses in all healthcare settings within LMICs. Studies published in English were included with no limit on publication date. Two independent reviewers screened titles, abstracts, and full-text articles of published studies. Data were analyzed quantitatively using frequencies and counts. Textual data from qualitative studies were analyzed using descriptive content analysis. RESULTS: Fifty-three publications were included, involving 20 LMICs. Studies were published between 2007 and 2023, with over 60% published in the past 7 years. Studies that evaluated familiarity/awareness of EBP showed that in general, nurses had low familiarity with or awareness of EBP. Most studies (60%) described nurses' attitudes toward EBP as positive, favorable, or high, and 31% as moderate. However, over 60% of studies described nurses' EBP knowledge/skills as moderate, low, or insufficient. Approximately 84% of studies described EBP implementation in healthcare settings as moderate, low, poor, or suboptimal. LINKING EVIDENCE TO ACTION: Studies on nursing EBP have been increasing in LMICs for the past two decades, with findings highlighting opportunities for advancing EBP in nursing within LMICs. Health systems and healthcare organization leaders in LMICs should equip nurses with EBP knowledge and skills while providing the needed resources and support to ensure consistent implementation of EBP to improve health outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".