Rewarding Success: Changing the Paradigm of How Research Is Rewarded
Bibliographic record
Abstract
International health system comparisons reveal that Canada ranks poorly in several measures when assessed against comparable countries, despite the fact that billions of dollars are spent on the Canadian healthcare system every year. Canada is among one of the highest spenders on health care, yet value for our investment is not always clear. To sustain Canadian health care, it is essential that innovations and process transformations that improve health outcomes and value for our investment are implemented in the health system. Following the movement of other organizations that are experimenting with innovative models of funding, the Canadian Institutes of Health Research partnered with four Canadian provinces to pilot the Rewarding Success Initiative. This initiative rewards and incentivizes research teams to develop effective partnerships with health system payers and, together, implement innovative solutions in the health system that will enhance value-based care, health system sustainability and health outcomes.
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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 | 0.332 | 0.378 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.011 | 0.151 |
| Scholarly communication | 0.046 | 0.052 |
| Open science | 0.010 | 0.022 |
| Research integrity | 0.023 | 0.030 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".