The concept of knowledge in KM: A knowledge domain process model applied to inter‐professional care
Bibliographic record
Abstract
Abstract Grounded in a social construction view of knowledge and based on the work of Haridimos Tsoukas and Max Boisot, this paper attempts to extend Colin Reilly's knowledge domain model using a process modeling approach. The objective of this paper is to construct a more comprehensive meta‐understanding of knowledge domains that considers the influence of organizational context, of a community or group (social construction of knowledge), the presence of events and knowledge artifacts, temporal cycles, and individual knowledge processes. The authors begin by synthesizing the existing literature review to construct the proposed Knowledge Domain Process (KDP) model. Following the construction of the model, the authors develop and use a composite case (from a number of cases experienced by one of the authors as a practitioner) to illustrate the application of the model. The proposed model is then applied to an Inter‐Professional Care (IPC) setting within health care, to illustrate how knowledge is constructed, exchanged, and used across numerous health care communities, in an effort to improve coordination and care. The KDP model attempts to provide researchers and practitioners with a more structured, detailed, and analytical way of looking at the processes involved in knowledge construction and dissemination. This model is viewed as a work‐in‐progress and is still under development. Use by others is encouraged and will help validate or refute the model in part or in whole. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".