Royal Society of Canada working group on health research system recovery: strengthening Canada’s health research system after the COVID-19 pandemic
Bibliographic record
Abstract
The Royal Society of Canada Working Group on Health Research System Recovery developed actionable recommendations for organizations to implement to strengthen Canada’s health research system. Recommendations were based on input from participants from G7 countries and Australia and New Zealand. Participants included health research funding agency leaders; research institute leaders; health, public health, and social care policy-makers; researchers; and members of the public. The recommendations were categorized using the World Health Organization’s framework for health research systems and include governance/stewardship: (1) Outline research logistics as part of emergency preparedness to streamline research in future pandemics. (2) Embed equity and inclusion in all research processes. (3) Facilitate streamlined, inclusive, and rigorous processes for grant application preparation and review. (4) Create knowledge mobilization infrastructure to support the generation and use of evidence. (5) Coordinate research efforts across local, provincial, national, and international entities. Financing: (6) Reimagine the funding of health research. Capacity building: (7) Invest in formative training opportunities rooted in equity, diversity, and anti-racism. (8) Support researchers’ career development throughout their career span. (9) Support early career researchers to establish themselves. Producing and using research: (10) Strengthen Indigenous health research and break down systemic barriers to its conduct. (11) Develop mechanisms to produce novel research. (12) Enhance research use across the health research ecosystem.
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.
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 | 0.197 | 0.176 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.029 | 0.010 |
| Open science | 0.023 | 0.025 |
| Research integrity | 0.028 | 0.025 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".