Strategizing Research for Impact
Bibliographic record
Abstract
In its Strategic Plan 2021-2026, the Canadian Institutes of Health Research - Institute of Health Services and Policy Research (IHSPR) convincingly expresses its desire to expand capacity for applied health services and policy research (HSPR) and better mobilize research results for health system transformation geared toward the Quadruple Aim and health equity for all (CIHR IHSPR 2021). These strategic priorities echo views widely shared within the HSPR community, and we commend IHSPR for its leadership and vision. Recognizing the systemic challenges ahead of us, this commentary considers the HSPR community's capacity to achieve the promise of learning health systems, given the obstacles likely to hinder their rapid scale-up over the next five years. Next, we consider the spread of virtual care during the pandemic to illustrate the embedded and negotiated nature of innovation in health systems and the need for vigilance as to the social distribution of their benefits and costs. Finally, a critical review of the strategic plan provides insights into how research is governed in the HSPR field. Based on this analysis, it appears essential to reconsider health system transformation as social system transformation and strengthen interdisciplinary and comparative research. Looking forward, developing a science of science to better understand the conditions associated with high-impact research should be a cross-cutting priority for Canada's HSPR community.
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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 | 0.200 | 0.184 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.027 | 0.033 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".