Barriers and facilitators of implementation of shared decision-making in clinical practice; An umbrella review
Notice bibliographique
Résumé
Abstract Background: Involving patients in their healthcare by means of shared decision-making (SDM) is promoted through policy and research. However, its implementation in routine practice has certain complexities and intricacies. This umbrella review was carried out to review literature exploring barriers and facilitators of the implementation of SDM and provide a comparative view of these factors in different settings. Methods: The search strategy was focused on peer-reviewed systematic reviews on the implementation of SDM with the primary aim of identifying as many as possible of facilitators and barriers. We systematically searched PubMed, Embase, and Web of Science from date of conception to August 2022. We included studies that reported providers' perspectives, while those focusing solely on patients' perspectives or mixing both patients' and providers' perspectives were excluded. We limited our focus to the studies published in the English language. Quality assessment of studies was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Results: Of the 5415 records found, titles and abstracts of 1932 articles were screened and 53 were reviewed in full text. 15 articles met the inclusion criteria. We then created a map of all items against the included papers and sorted them into 7 general categories and 4 specialized categories. Our categorization was devised to represent a root cause and solution-based approach and provide a clear and meaningful framework for understanding the factors influencing the successful integration of SDM in healthcare settings. The general categories include: 1) Provider-related: Encompasses factors such as provider attitude and skill of SDM. 2) Patient-related: Includes patient factors, such as literacy, and willingness to engage in decision-making. 3) Environmental context: Addresses the physical clinical setting and personnel. 4) Disease-related: Addresses the impact of clinical nature of diseases on SDM adoption. 5) Administrative: Involves logistical and organizational factors, workflow alignment. 6) Access and trust to knowledge or tools: Focuses on accessibility to reliable resources and decision aids. 7) Social interaction and cultural: Emphasizes social influences and cultural norms. The specialized categories include: 1) pediatrics 2) screening (which mostly comprises the screening carried out in primary care) 3) end-of-life ICU care 4) mental healthcare. Overall, the most cited items were time constraints, physicians’ knowledge, and physicians’ skills in SDM. Conclusion: The implementation of SDM is still comparatively young; many studies have been conducted yet there is limited research focusing on specific settings and specialties. Furthermore, quantitative research on the subject is very scarce. Organizations and health policymakers aiming to implement SDM can benefit from considering factors gathered in our study for better planning. Registration: The protocol for this study was registered to Tabriz University of Medical Sciences research vice as a thesis proposal with the following number: 64957. The English version has been registered in Open Science Framework. (DOI: 10.17605/OSF.IO/GZNA4) Keywords: Shared Decision making, Implementation, Barriers and Facilitators
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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 | 0,059 | 0,188 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,007 |
| Bibliométrie | 0,033 | 0,025 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,012 | 0,011 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».