Does Knowledge Management Really Work?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Contemporary organizations, including those involved with healthcare, are constantly under pressure to produce and implement new strategies for delivering better products and/or services. Knowledge Management (KM) has been one of the paradigms successfully applied in such business environs. However, a lack of proper application of KM principles and its components have reduced the confidence of new adopters of this paradigm. KM-based healthcare projects are moving forward, and innovation is the driving force behind such initiatives. This chapter sets the scene by outlining the KM’s core elements, facets and how they can be appropriately applied within an innovative, real-time healthcare project. It further enumerates a case study which targets the screening attendance issue for the NHS’ breast screening program. The case study not only discusses the need of a balanced approach to address both the technological and humanistic aspects of KM, but also answers the question “Does knowledge management really work?” A questionnaire-based study was conducted with the General Physicians (GPs) on the KM’s aspects and its relationship to the interventions proposed in the study. The study provided ample proof that a balanced approach will definitely increase the efficacy of such initiatives. Such studies can increase the confidence of future KM adopters in healthcare domain. This chapter provides credibility for such balanced KM-based initiatives and highlights the importance of a focused approach on the various facets of KM to maximize benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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 it