Using a rapid environmental scan methodology to map country-level global health research expertise in Canada
Bibliographic record
Abstract
BACKGROUND: Many countries are currently rethinking their global health research funding priorities. When resources are limited, it is important to understand and use information about existing research strengths to inform research strategies and investments and to drive impact. This study describes a method to rapidly assess a country's global health research expertise and applies this method in the Canadian context. METHODS: We developed a three-pronged rapid environmental scan to evaluate Canadian global health research expertise that focused on research funding inputs, research activities and research outputs. We assessed research funding inputs from Canada's national health research funding agency and identified the 30 Canadian universities that received the most global health research funding. We systematically searched university websites and secondary databases to identify research activities, including research centres, research chairs and research training programmes. To evaluate research outputs, we searched PubMed to identify global health research publications by Canadian university-affiliated researchers. We used these three perspectives to develop a more nuanced understanding of Canadian strengths in global health research from different perspectives. RESULTS: Canada's main global health research funder, the Canadian Institutes of Health Research, invested a total of $314 M from 2000 to 2016 on global health research grants. This investment has contributed to Canada's wealth of global health research expertise, including 12 training programmes, 27 Canada Research Chairs, 6 research centres and 30 WHO Collaborating Centres across 27 universities. Research activities were concentrated in Canada's biggest cities and most commonly focused on health equity and globalisation issues. Canadian-affiliated researchers have contributed to a research output of 822 unique publications on PubMed. There is an opportunity to build global health expertise in regions not already concentrated with research activity, focusing on transnational risks and neglected conditions research. CONCLUSIONS: Our three-pronged approach allowed us to rapidly identify clear geographic and substantive areas of strength in Canadian global health research, including urban regions and research focused on health equity and globalisation topics. This information can be used to support research policy directives, including to inform a Canadian global health research strategy, and to allow relevant academic institutions and funding organisations to make more strategic decisions regarding their future investments.
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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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.052 | 0.074 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".