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Enregistrement W4315754134 · doi:10.11124/jbies-22-00433

How “gutsy” does an organization have to be to absorb new information?

2023· editorial· en· W4315754134 sur OpenAlexaffabout
Christina Godfrey, Andrea C. Tricco, Rosemary Wilson, Kim Sears

Notice bibliographique

RevueJBI Evidence Synthesis · 2023
Typeeditorial
Langueen
DomaineMedicine
ThématiqueHealth and Medical Research Impacts
Établissements canadiensSt. Michael's HospitalQueen's University
Organismes subventionnairesnon disponible
Mots-clésAbsorptive capacityKnowledge managementProcess (computing)Body of knowledgeBusinessComputer science

Résumé

récupéré en direct d'OpenAlex

Absorptive capacity was first defined in 1990 by Cohen and Levinthal as “an organization’s ability to identify, assimilate, and integrate new knowledge.”1(p.128) In 2002, Zahra and George2 expanded on this definition to emphasize the active process of transforming and exploiting that new knowledge.3 Their extended definition provided the basis for the 4 components of absorptive capacity: i) identify the new knowledge, ii) assimilate the knowledge, iii) integrate the knowledge into the existing knowledge base, and iv) use the new knowledge to change the organization in some way.3 The concept of absorptive capacity has its origins in biology4 and refers to the capacity of the gastrointestinal tract to absorb digested nutrients into the body. Somehow, this term leaked out of the gut and has been adopted by the business world where it is used to help understand and monitor an organization’s capacity to absorb new or innovative knowledge.1 In due course, the health care system also adopted this concept, hence the reason behind our review published in this issue of JBI Evidence Synthesis.3 In preparation for our scoping review, we engaged with all stakeholders and explored our understanding of the concept of absorptive capacity. Then, we examined the literature, seeking to establish how absorptive capacity is conceptualized and measured in the adoption of innovations in health care organizations. A scoping review methodology was deemed the most appropriate methodology to pursue this investigation, as it facilitated the breadth of inquiry required to inform both of these related issues. The review was conducted through the Strategy for Patient Oriented Research (SPOR) Evidence Alliance5 and was initiated by the Knowledge Translation Unit, Strategic Policy Branch of Health Canada. Colleagues from Health Canada were involved at multiple key points throughout the review.6 Most importantly, the entire team, including the Health Canada library scientist, Health Canada director, the Queen’s Collaboration for Health Care Quality research team, SPOR Evidence Alliance staff, and several graduate students, collaborated over multiple meetings to generate and refine the research question. Knowledge-user experts in the field of organizational change were also invited and included in the meeting discussions. Beyond refining the question, this integrated knowledge translation approach7 involved title and abstract screening, full-text review, and editorial contributions to the final report.6 None of the articles included in this review generated a new definition of absorptive capacity; rather, they investigated the influence of local health care contexts on this process. Each context required a different focus on the 4 components of absorptive capacity, and organizations drew attention to the factors that they found valuable to their particular process. It appears that organizations need to be more than just “gutsy” to absorb new information; expertise in each of the components of absorptive capacity is important to the success of this process, and understanding the contribution of context is essential. Measurement of the progress is crucial, and using the domains of absorptive capacity as a framework is beneficial in guiding this process of planning for and assessing the adoption of knowledge within the organization and any change that ensues as a result.

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 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)0,003
score de la tête « metaresearch » (Gemma)0,796
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 consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,793
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,796
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,002

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.

Tête enseignante Opus0,053
Tête enseignante GPT0,404
Écart entre enseignants0,350 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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