Life as a knowledge broker in public health
Bibliographic record
Abstract
Program objective – Knowledge brokers (KBs), like clinical librarians (CLs), are information professionals involved in the promotion of evidence-informed decision-making (EIDM). As with CLs, the impact of literature-evaluating KBs on the health sector is sparse, and there is limited consensus on their role. To provide guidance to information professionals and organizations regarding the KB role, this paper describes a typical “day in the life” of a KB in delivering a program to promote EIDM among Canadian public health professionals. Setting – The KB program was implemented in a randomized controlled trial evaluating knowledge transfer and exchange strategies. Participants – Public health managers at various levels within Canadian public health units or regional health authorities. Program – The KB identified decision makers’ (DMs) evidence needs; facilitated access to and use of high-quality evidence; assisted in decision making; and facilitated EIDM skill development. Results – The KB role, activities and related tasks, lessons learned, and challenges are described. Conclusion – Central themes included the importance of relationship development, ongoing support, customized approaches, and individual and organizational capacity development. The novelty of the KB role in public health provided a unique opportunity to assess the need for and reaction to the role and its associated activities.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".