Bibliographic record
Abstract
The Canadian Institutes of Health Research's (CIHR), Canada's premier health research funding agency, is moving forward in realizing a more systematic, ongoing integration of citizens' input in priority setting, governance and funding programs and tools. In 2008, the Canadian Institutes of Health Research (CIHR) developed a Framework for Citizen Engagement. This Framework establishes guidelines for implementing a more systematic approach to consulting and engaging citizens, such as in assessing the merit and relevance of research applications, developing strategic plans, setting research priorities and for strengthening their role on CIHR's governance committees. This paper describes the current context for public consultation in Canada's federal health care system, the new CIHR citizen engagement framework and discusses citizen engagement activities and efforts undertaken by CIHR institutes and branches. It reviews the methods used by CIHR to engage citizens in four key focus areas: 1. Representation on CIHR's Boards and Committees; 2. Corporate and Institute strategic plans, priorities, policies, and guidelines; 3. Research priority setting and integrated knowledge translation; 4. Knowledge dissemination and public outreach. In discussing CIHR's experiences, the paper identifies some of the challenges and benefits of engaging citizens in CIHR's research processes, including participating in decision making and informing strategic priorities.
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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 | 0.224 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.033 | 0.041 |
| Scholarly communication | 0.033 | 0.016 |
| Open science | 0.004 | 0.049 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 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".