Clarifying the concepts in knowledge transfer: a literature review
Bibliographic record
Abstract
AIM: The aim of this paper is to examine the concepts of opinion leaders, facilitators, champions, linking agents and change agents as described in health, education and management literature in order to determine the conceptual underpinnings of each. BACKGROUND: The knowledge utilization and diffusion of innovation literature encompasses many different disciplines, from management to education to nursing. Due to the involvement of multiple specialties, concepts are often borrowed or used interchangeably and may lack standard definition. This contributes to confusion and ambiguity in the exactness of concepts. METHODS: A critical analysis of the literature was undertaken of the concepts opinion leaders, facilitators, champions, linking agents and change agents. A literature search using the concepts as keywords was conducted using Medline, CINAHL, Proquest and ERIC from 1990 to March 2003. All papers that gave sufficient detail describing the various concepts were included in the review. Several 'older' papers were included as they were identified as seminal work or were frequently cited by other authors. In addition, reference lists were reviewed to identify books seen by authors as essential to the field. FINDINGS: Two similarities cut across each of the five roles: the underlying assumption that increasing the availability of knowledge will lead to behaviour change, and that in essence each role is a form of change agent. There are, however, many differences that suggest that these concepts are conceptually unique. CONCLUSIONS: There is inconsistency in the use of the various terms, and this has implications for comparisons of intervention studies within the knowledge diffusion literature. From these comparisons, we concluded that considerable confusion and overlap continues to exist and these concepts may indeed be similar phenomena with different labels. All concepts appear to be based on the premise that interpersonal contact improves the likelihood of behavioural change when introducing new innovations into the health sector.
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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 | 0.039 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.030 | 0.038 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".