Closing the loop: From system-based data to evidence-influenced policy and practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it