Assessing KM Capabilities in two African Healthcare Organizations: Case Study
Notice bibliographique
Résumé
This study aims to better understand the process for the development of organizational capabilities specific to knowledge management (KMC) in the context of healthcare organizations. This process lies within the framework of apprenticeship training that promotes a process for organizational training and knowledge acquisition that can be spread over time and at different levels of intellectual development. Healthcare organizations are among those organizations that still struggle to adequately use the existing knowledge of their employees, due to the lack of good knowledge management. Although most of them are modernizing with computers and new technologies, is there effective knowledge management of employees, and what is their level of KMC? Besides, massive data and information is collected every day in health facilities, do they use it for effective decision-making and to strengthen their knowledge? This paper presents an analysis and develops a model that presents five levels of intellectual progress using the KMC maturity model as a development model to assess the KMC levels of two hospital organizations in the Democratic Republic of Congo which is one of the countries of sub-Saharan Africa. Our model includes three dimensions: 1. knowledge infrastructures in knowledge management; 2) knowledge management process; 3) knowledge management competency. These three dimensions aim to seek improvements or to develop the KMC of our studied health facilities. Finally, we wish to emphasize that the conclusions of this study are not representative of quantitative research but rather qualitative research that aims to comprehend the phenomenon of the knowledge management capabilities (KMC) in a context through this case study. From a practical point of view, this article provides for the identification of factors that influence the nature and effectiveness of the use of KMC in healthcare facilities. Also, promote the use of the KMC maturity model as a model for evaluating health organizations aimed at helping the health sector to set new standards for information flow and to manage their KM well. This paper presents an analysis and develops a model of the factors that influence unlearning focused on the healthcare industry. It is comprised of three constituent components: 1) a framework characterizing the lens through which individuals view situations; 2) a framework for characterizing how individual habits change and 3) a framework for characterizing the manner in which emergent understandings are consolidated into existing knowledge and knowledge structures. This paper presents an analysis and develops a model of the factors that influence unlearning focused on the healthcare industry. It is comprised of three constituent components: 1)a framework characterizing the lens through which individuals view situations;2)a framework for characterizing how individual habits change and 3) a framework for characterizing the manner in which emergent understandings are consolidated into existing knowledge and knowledge structures.
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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 | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| 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 ».