Systematic analysis of global health research funding in Canada, 2000–2016
Bibliographic record
Abstract
OBJECTIVES: Considering recent shifts in global funding landscapes, this study analyzes Canada's long-term global health research funding trends in the hope of informing a new Canadian global health research strategy. Examining past investments can help prioritize limited future resources to either build on Canada's existing strengths or fill gaps where needed, while simultaneously informing the investments of research funders in other countries. METHODS: Administrative data were analyzed covering all 1584 global health research grants awarded by the Canadian Institutes of Health Research (CIHR) to 927 unique principal investigators from 2000 to 2016, totalling C$341 million. Existing metadata associated with each grant was supplemented by additional qualitative coding. Descriptive time-series analyses of global health research grant data were conducted using various measures related to each grant's recipient (e.g., province, university, sex, distribution) and subject matter (e.g., research theme, area, focus). RESULTS: CIHR's total annual global health research funding increased sharply from $3.6 million in FY2000/2001 to $30.3 million in FY2015/2016, with the largest share of research funding now focused on health equity-representing nearly 50% of CIHR's global health research funding. Past grants have concentrated on infectious disease and public health research. One third of CIHR's global health grant funding went to 20 principal investigators. Only 42.2% of global health research funding came from CIHR's open investigator-driven competitions, with the rest coming from strategic priority-driven competitions. CONCLUSION: Global health research has seen steady increases in funding from CIHR's open competitions when preceded by investment in strategic competitions, which suggests the level of a national research funding agency's strategic investments in global health research may determine the size of the field in their country. The greatest concentration of past investment lies in health equity research, followed by infectious disease research. Future analyses of research funding would benefit from an internationally accepted keyword classification scheme and more granular administrative data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".