Getting the message across: Principles for developing brief-Knowledge Transfer (b-KT) communiqués
Bibliographic record
Abstract
OBJECTIVE: This feature article on knowledge transfer presents principles and strategies to support the development of short communiqués to end-users. PARTICIPANTS: Formal and informal knowledge brokers are the targeted users of the strategies. METHODS: Research studies and conceptual literature in knowledge transfer informed the development of brief-Knowledge Transfer (b-KT) principles. Principles are explained and a sample of how they informed the development of KIT-Tip Sheets is offered to promote ways to use principles in knowledge dissemination. RESULTS: b-KT principles can be used as a framework to guide the development of short communiqués by knowledge brokers in work practice but also in the health, social and rehabilitation domains. In addition, these principles promote the participation of end-users in the development of knowledge transfer. CONCLUSIONS: Formal evaluation is needed on the use of these principles in achieving the uptake and use of knowledge by end-users.
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.
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 | 0.128 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".