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Enregistrement W4290759908 · doi:10.11124/jbies-21-00436

Absorptive capacity in the adoption of innovations in health: a scoping review

2022· review· en· W4290759908 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.
fundUn bailleur canadien est enregistré sur le travail.

Notice bibliographique

RevueJBI Evidence Synthesis · 2022
Typereview
Langueen
DomaineHealth Professions
ThématiqueHealth Policy Implementation Science
Établissements canadiensHealth CanadaSt. Michael's HospitalQueen's University
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésAbsorptive capacityBusinessKnowledge managementComputer scienceIndustrial organization

Résumé

récupéré en direct d'OpenAlex

OBJECTIVE: The objective of this scoping review was to explore how absorptive capacity has been conceptualized and measured in studies of innovation adoption in health care organizations. INTRODUCTION: Current literature highlights the need to incorporate knowledge translation processes at the organizational and system level to enhance the adoption of new knowledge into practice. Absorptive capacity is a set of routines and processes characterized by knowledge acquisition, assimilation, transformation, and application. A key concept in organizational learning theory, absorptive capacity is thought to be critical to the adoption of new knowledge and innovations in organizations. To understand how absorptive capacity was conceptualized and measured in health care organizations, it was appropriate to conduct a scoping review to answer our research question. INCLUSION CRITERIA: This scoping review included published and unpublished primary studies (ie, experimental, quasi-experimental, observational, and qualitative study designs), as well as reviews that broadly focused on the adoption of innovations at the organizational level in health care, and framed innovation adoption as processes that rely on organizational learning and absorptive or learning capacity. METHODS: Searches included electronic databases (ie, MEDLINE, Embase, PsycINFO, CINAHL, and Scopus) and gray literature, as well as reference scanning of relevant studies. Study abstracts and full texts were screened for eligibility by two independent reviewers. Data extraction of relevant studies was also done independently by two reviewers. All discrepancies were addressed through discussion or adjudicated by a third reviewer. Synthesis of the extracted data focused on descriptive frequencies and counts of the results. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). RESULTS: The search strategies identified a total of 7433 citations. Sixteen papers were identified for inclusion, including a set of two companion papers, and data were extracted from 15 studies. We synthesized the objectives of the included studies and identified that researchers focused on at least one of the following aspects: i) exploring pre-existing capacity that affects improvement and innovation in health care settings; ii) describing factors influencing the spread and sustainability of organizations; iii) identifying measures and testing the knowledge application process; and iv) providing construct clarity. No new definitions were identified within this review; instead existing definitions were refined to suit the local context of the health care organization in which they were used. CONCLUSIONS: Given the rapidly changing and evolving nature of health care, it is important to understand both current best practices and an organization's ability to acquire, assimilate, transform, and apply these practices to their specific organization. While much research has gone into developing ways to implement knowledge translation, understanding an organization's internal structures and framework for seeking out and implementing new evidence as it relates to absorptive capacity is still a relatively novel concept.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,035
score de la tête « metaresearch » (Gemma)0,048
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,401
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0350,048
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0010,006
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,762
Tête enseignante GPT0,672
Écart entre enseignants0,090 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle