The Ontario Drug Policy Research Network: Bridging the gap between Research and Drug Policy
Bibliographic record
Abstract
Policymakers have cited several barriers to using evidence in policy decisions, including lack of research relevance and timeliness. In recent years, several reports have focused on the successes and challenges of researcher-policymaker collaborations, a form of policy engagement intended to help overcome barriers to the use of research evidence in policymaking. Although these reports often demonstrate an increase in research relevance, rarely do they provide concrete methods of enhancing research timeliness, which is surprising given policymakers' expressed need to receive "rapid-response" research. Additionally, the impact of researcher-policymaker collaborations is not well-discussed. In this paper, we aim to describe the collaboration between the Ontario Drug Policy Research Network (ODPRN) and its policymaker partner, the Ontario Public Drug Program (OPDP), with a particular focus on the ODPRN's research methodology and unique rapid-response approach for policy engagement. This approach is illustrated through a specific case example regarding drug funding policies for pulmonary arterial hypertension. Moreover, we discuss the impact of the ODPRN's research on pharmaceutical policy and lessons learned throughout the ODPRN and OPDP's five-year partnership. The described experiences will be valuable to those seeking to enhance evidence uptake in policymaking for immediate policy needs.
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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 | 0.096 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".