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Enregistrement W2916578330 · doi:10.1108/jkm-10-2018-0636

Overcoming knowledge barriers to health care through continuous learning

2019· article· en· W2916578330 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.

Notice bibliographique

RevueJournal of Knowledge Management · 2019
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueKnowledge Management and Sharing
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésKnowledge managementHealth careAssimilation (phonology)BureaucracyBusinessComputer scienceProcess managementPolitical science

Résumé

récupéré en direct d'OpenAlex

Purpose The purpose of this study is to explore the role of continuous learning and the mitigation or elimination of knowledge barriers affecting information technology (IT) assimilation in the health-care sector. Most of the problems with IT assimilations stem from a poor understanding of the nature of suitable information, the lack of trust, cultural differences, the lack of appropriate training and hierarchical bureaucratic structures and procedures. To overcome these barriers, this study provides evidence that a continuous learning process can play a part in overcoming some of the obstacles to the assimilation of IT. Design/methodology/approach This study investigates how a continuous learning environment can counteract the presence of knowledge barriers, and, along with such an environment, can, in turn, facilitate IT assimilation. The study uses ADANCO 2.0.1 Professional for Windows and involves the collection and analysis of data provided by 210 health-care end users. Findings The study provides evidence in support of the proposition that continuous learning may facilitate the assimilation of IT by health-care end users through the mitigation of knowledge barriers (e.g. lack of trust or resistance to change). The mitigation of these barriers requires the gathering and utilization of new knowledge and knowledge structures. The results support the hypothesis that one way in which this can be achieved is through continuous learning (i.e. through assessing the situation, consulting experts, seeking feedback and tracking progress). Research limitations/implications A limitation of the study is the relatively simple statistical method that has been used for the analysis. However, the results provided here will serve as a preliminary basis for more sophisticated analysis which is currently underway. Practical implications The study provides useful insights into ways of using continuous learning to facilitate IT assimilation by end users in the health-care domain. This can be of use to hospitals seeking to implement end user IT technologies and, in particular, telemedicine technologies. It can also be used to develop awareness of knowledge barriers and possible approaches to mitigate the effects of such barriers. Such an awareness can assist hospital staff in finding creative solutions for using technology tools. This potentially augments the ability of hospital staff to work with patients and carers, encouraging them to take initiative (make choices and solve problems relevant to them). This, in turn, allows hospitals to avoid negative and thus de-motivating experiences involving themselves and their end users (patients) and improving IT assimilation. This is liable to lead to improved morale and improved assimilation of IT by end users (patients). Social implications As ICT systems and services should entail participation of a wide range of users, developers and stakeholders, including medical doctors, nurses, social workers, patients and programmers and interaction designers, the study provides useful social implication for health management and people well-being. Originality/value The paper contributes to a better understanding of the nature and impacts of continuous learning. Although previous studies in the field of knowledge management have shown that knowledge management procedures and routines can provide support to IT assimilation, few studies, if any, have explored the relationship between continuous learning and IT assimilation with particular emphasis on knowledge barriers in the health-care domain.

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)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-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: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,900
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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 Opus0,018
Tête enseignante GPT0,327
Écart entre enseignants0,309 · 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