Assessing KM Capabilities in two African Healthcare Organizations: Case Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".