Closing the loop: From system-based data to evidence-influenced policy and practice
Bibliographic record
Abstract
For more than 30 years, the Manitoba Centre for Health Policy has been conducting research and evaluation to provide timely and critical evidence to answer real-world policy questions. Our experienced team of research scientists, analysts and other staff work extensively with policy-makers at the macro, meso and micro levels of government to support evidence-informed policy and program development in an effort to ensure that policy initiatives provide the greatest benefit possible to individuals and society as a whole. Using the widely recognized whole-population Manitoba Population Research Data Repository, which comprises approximately 100 different datasets from multiple sectors, we employ sophisticated and state-of-the-art research methods and data science technologies, and then translate the results into meaningful insights or recommendations for policy-makers. Our long and productive history of working with policy-makers has taught us much about making our research relevant to policy-makers. In this article, we outline some examples of how research evidence has been used to influence policy in Manitoba, and the key lessons we have learned about what makes relationships between researchers and policy-makers work. In essence, policy-makers have supported the growth of the Repository over the last 30 years, because researchers have "closed the loop" by sharing valuable and policy-relevant research results with them. This ability to inform policies, programs and service delivery with scientific evidence continues to benefit individuals, communities and our society as a whole.
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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 | 0.415 | 0.569 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.059 | 0.062 |
| Open science | 0.014 | 0.041 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".