How much are we spending? The estimation of research expenditures on cardiovascular disease in Canada
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is a leading cause of death in Canada and is a priority area for medical research. The research funding landscape in Canada has changed quite a bit over the last few decades, as have funding levels. Our objective was to estimate the magnitude of expenditures on CVD research for the public and charitable (not-for profit) sectors in Canada between 1975 and 2005. METHODS: To estimate research expenditures for the public and charitable sectors, we compiled a complete list of granting agencies in Canada, contacted each agency and the Canadian Institutes of Health Research (CIHR), and extracted data from the organizations' annual reports and the Reference Lists of health research in Canada. Two independent reviewers scanned all grant and fellowship/scholarship titles (and summary/key words, when available) of all research projects funded to determine their inclusion in our analysis; only grants and fellowships/scholarships that focused on heart and peripheral vascular diseases were selected. RESULTS: Public/charitable sector funding increased 7.5 times, from close to $13 million (in constant dollars) in 1975 to almost $96 million (in constant dollars) in 2005 (base year). The Medical Research Council of Canada (MRCC)/CIHR and the Heart & Stroke Foundation of Canada have been the main founders of this type of research during our analysis period; the Alberta Heritage Foundation for Medical Research and the Fonds de la recherche en santé du Quebec have played major roles at the provincial level. The Indirect Costs Research Program and Canada Foundation for Innovation have played major roles in terms of funding in the last years of our analysis. CONCLUSION: Public/charitable-funded research expenditures devoted to CVD have increased substantially over the last three decades. By international standards, the evidence suggests Canada spends less on health-related research than the UK and the US, at least in absolute terms. However, this may not be too problematic as Canada is likely to free-ride from research undertaken elsewhere. Understanding these past trends in research funding may provide decision makers with important information for planning future research efforts. Future work in this area should include the use of our coding methods to obtain estimates of funded research for other diseases in Canada.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".