[An exploratory synthesis of knowledge brokering in public health].
Bibliographic record
Abstract
There is a call for public health policies and interventions to be evidence-based. Also, using knowledge brokers to foster the use of research results is increasingly recommended. This article presents an exploratory synthesis of the current state of knowledge on this new strategy We conducted a scoping study by consulting the main databases. Nineteen articles were included in the analysis, which was designed with a grid developed iteratively. The synthesis shows that knowledge brokering initiatives include i) planning activities (stakeholder identification, creation of networks and partnerships, context analysis, problem identification, needs identification), ii) support to the brokers (training, technical support, development of a practice guide), and iii) the brokerage activities themselves (information management, liaison between knowledge producers and users, training of users). Only four articles presented empirical data on the effects of brokers' activities. Three were associated with increased knowledge in the target audience. No study showed any impact on clinical behaviours or on public policy content. This synthesis highlights the challenges involved in knowledge brokering activities, as well as the characteristics and skills a broker should possess. While knowledge brokering appears promising, efforts must now be made to evaluate it more systematically to demonstrate its effectiveness.
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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 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.051 | 0.076 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".