Knowledge translation strategies to support the sustainability of evidence-based interventions in healthcare: a scoping review
Notice bibliographique
Résumé
BACKGROUND: Knowledge translation (KT) strategies are widely used to facilitate the implementation of EBIs into healthcare practices. However, it is unknown what and how KT strategies are used to facilitate the sustainability of EBIs in institutional healthcare settings. OBJECTIVES: This scoping review aimed to consolidate the current evidence on (i) what and how KT strategies are being used for the sustainability of EBIs in institutional healthcare settings; (ii) the reported KT strategy outcomes (e.g., acceptability) for EBI sustainability, and (iii) the reported EBI sustainability outcomes (e.g., EBI activities or component of the intervention continue). METHODS: We conducted a scoping review of five electronic databases. We included studies describing the use of specific KT strategies to facilitate the sustainability of EBIs (more than 1-year post-implementation). We coded KT strategies using the clustered ERIC taxonomy and AIMD framework, we coded KT strategy outcomes using Tierney et al.'s measures, and EBI sustainability outcomes using Scheirer and Dearing's and Lennox's taxonomy. We conducted descriptive numerical summaries and a narrative synthesis to analyze the results. RESULTS: The search identified 3776 studies for review. Following the screening, 25 studies (reported in 27 papers due to two companion reports) met the final inclusion criteria. Most studies used multi-component KT strategies for EBI sustainability (n = 24). The most common ERIC KT strategy clusters were to train and educate stakeholders (n = 38) and develop stakeholder interrelationships (n = 34). Education was the most widely used KT strategy (n = 17). Many studies (n = 11) did not clearly report whether they used different or the same KT strategies between EBI implementation and sustainability. Seven studies adapted KT strategies from implementation to sustainability efforts. Only two studies reported using a new KT strategy for EBI sustainability. The most reported KT strategy outcomes were acceptability (n = 10), sustainability (n = 5); and adoption (n = 4). The most commonly measured EBI sustainability outcome was the continuation of EBI activities or components (n = 23), followed by continued benefits for patients, staff, and stakeholders (n = 22). CONCLUSIONS: Our review provides insight into a conceptual problem where initial EBI implementation and sustainability are considered as two discrete time periods. Our findings show we need to consider EBI implementation and sustainability as a continuum and design and select KT strategies with this in mind. Our review has emphasized areas that require further research (e.g., KT strategy adaptation for EBI sustainability). To advance understanding of how to employ KT strategies for EBI sustainability, we recommend clearly reporting the dose, frequency, adaptations, fidelity, and cost of KT strategies. Advancing our understanding in this area would facilitate better design, selection, tailored, and adapted use of KT strategies for EBI sustainability, thereby contributing to improved patient, provider, and health system outcomes.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,132 | 0,323 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,009 | 0,011 |
| Bibliométrie | 0,048 | 0,036 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,014 | 0,018 |
| Science ouverte | 0,005 | 0,010 |
| Intégrité de la recherche | 0,006 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».