Getting evidence to travel inside public systems: what organisational brokering capacities exist for evidence-based policy?
Bibliographic record
Abstract
BACKGROUND: Implementing research findings into healthcare policy is an enduring challenge made even more difficult when policies must be developed and implemented with the help and support of multiple ideas, agendas and actors taking part in determinants of health. Only looking at mechanisms to feed policy-makers with evidence or to interest researchers in the policy process will simply bring partial clues; implementing evidence-based policy also requires organisations to lead and to partner in the production and intake of scientific evidence from academics and practical evidence from one another. MAIN BODY: This Commentary argues for the need to better understand the capacities required by organisations to foster evidence-based policy in a dispersed environment. It proposes a framework of 11 brokering capacities for organisations involved in evidence-based policy. Eight of these capacities are informed by streams of research related to the roles of knowledge broker, innovation broker and policy broker. Three complementary brokering capacities are informed by our experience studying real-life evidence-based policies; these are capturing boundary knowledge, trending know-how on scientific and practical evidence-based policy, and conveying evidence outward. CONCLUSIONS: Previous guidelines on brokering capacities focused on the individual level more than on the organisational level. Beyond the individual capacities of managers, designers and implementers of new policies, there is a need to identify and assess the brokering capacities of organisations involved in evidence-based policy. The three specific organisational brokering capacities for evidence-based policy that we present offer a means for policy-makers and policy designers to reflect upon favourable environments for evidence-based policy. These capacities could also help administrators and implementation scholars to think about and develop measurements to assess the quality and readiness of organisations involved in evidence-based policy design.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".