LEARNING HISTORY AS A CATALYST TO KNOWLEDGE TRANSFER IN HEALTH CARE ORGANIZATIONS Summary
Bibliographic record
Abstract
The importance of knowledge transfer to an organization’s competitiveness has been well documented. Yet knowledge transfer remains a complex, dynamic process that is far from easy to implement. This paper explores the value of the learning history methodology as a tool to accelerate knowledge transfer and organizational change within organizations. The learning history methodology is a qualitative research methodology that considers human perceptions, actions, opinions, and evaluations and was first designed to help transfer learnings from pilot projects to other parts of an organization. It is typically used within an action research environment, allowing recognition of what has been learned in the past to guide stakeholders in the dialogical generation of a new future. This paper first provides an overview of the literature on the learning history method, followed by an analysis of its application to an ongoing research project at the Eastern Townships Rehabilitation Centre in Quebec. The early results from this research project demonstrate how the learning history method has helped senior managers recognize and address the challenges involved in implementing change and transferring new knowledge in this organization. The learning history process identified employee concerns about change initiatives at the centre, and resulted in necessary modifications to the original implementation plan. The learning history methodology can therefore act as a catalyst to accelerate the knowledge transfer process within organizations.
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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 | 0.046 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".