Straw into Gold: Lessons Learned (and Still Being Learned) at the Manitoba Centre for Health Policy
Bibliographic record
Abstract
What lessons have we learned at the Manitoba Centre for Health Policy (MCHP) about knowledge translation (KT) over the past 20 years, and what is our vision for the future? How does that KT interrelate with our other activities - research and the Population Health Data Repository? Who first noticed that "there's gold in them thar hills," and what did they do about it? How did we weave administrative database "straw" into gold, how have we panned for gold and how do we look for the pot of gold in the future? This paper describes how MCHP began with an integrated KT research relationship with government, and through The Need to Know Team, extended KT to regional health authority planners. It describes the various push-pull KT mechanisms that MCHP has used, including dissemination of research to planners through interactive workshops, and to other researchers through Web-based resources.
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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 | 0.075 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".