Knowledge, skills, attitudes, beliefs, and implementation of evidence‐based practice among nurses in low‐ and middle‐income countries: A scoping review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».