Evaluation of Funding Gastroenterology Research in Canada Illustrates the Beneficial Role of Partnerships
Bibliographic record
Abstract
BACKGROUND: Funders of health research in Canada seek to determine how their funding programs impact research capacity and knowledge creation. OBJECTIVE: To evaluate the impact of a focused grants and award program that was cofunded by the Canadian Institutes of Health Research Institute of Nutrition, Metabolism and Diabetes, and the Canadian Association of Gastroenterology; and to measure the impact of the Program on the career paths of funded researchers and assess the outcomes of research supported through the Program. METHODS: A survey of the recipients of grants and awards from 2000 to 2008 was conducted in 2012. The CIHR Funding Decisions database was searched to determine subsequent funding; a bibliometric citation analysis of publications arising from the Program was performed. RESULTS: Of 160 grant and award recipients, 147 (92%) completed the survey. With >$17.4 million in research funding, support was provided for 131 fellowship awards, seven career transition awards, and 22 operating grants. More than three-quarters of grant and award recipients continue to work or train in a research-related position. Combined research outputs included 545 research articles, 130 review articles, 33 book chapters and 11 patents. Comparative analyses indicate that publications supported by the funding program had a greater impact than other Canadian and international comparators. CONCLUSIONS: Continuity in support of a long-term health research funding partnership strengthened the career development of gastroenterology researchers in Canada, and enhanced the creation and dissemination of new knowledge in the discipline.
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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 | 0.078 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.029 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".