Knowledge brokering in public health: A critical analysis of the results of a qualitative evaluation
Bibliographic record
Abstract
Empirical data on the processes underlying knowledge brokering (KB) interventions, including their determining factors and effects, remain scarce. Furthermore, these interventions are rarely built on explicit theoretical foundations, making their critical analysis difficult, even a posteriori. For these reasons, it appeared relevant to revisit the results of a qualitative evaluation undertaken in the province of Quebec in parallel with a Canada-wide randomized controlled trial (RCT) evaluating various KB strategies in public health. This paper looks critically at the theoretical foundations of the KB interventions in light of two conceptual models: (1) the dissemination model underlying the KB interventions used in the Canadian trial and (2) a systemic KB model developed later. This critical analysis sheds light on the processes involved in KB interventions and the factors influencing their implementation and effects. The conclusions of the critical analysis are consistent with the systemic model, in which interpersonal contact is an essential condition for effective KB interventions. This analysis may advance knowledge in the field by enhancing our understanding of the role of knowledge brokers as essential mediators in KB processes and outcomes.
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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 | 0.386 | 0.471 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".