Fostering Scientific Collaborations for Cancer Research Between High and Low/Middle Income Countries Through International Partnerships
Bibliographic record
Abstract
Background: Much remains to be learned about the causes of several major cancers. Implementing and sustaining global initiatives aimed to advance cancer research requires concerted efforts among government agencies, the industry and philanthropic institutions. Aiming to tackle this challenge, in 2015 the Azrieli Foundation, Canada's International Development Research Centre, the Canadian Institutes of Health Research, and the Israel Science Foundation launched the Joint Canada-Israel Health Research Program (JCIHRP), a 7-year CA$35 million partnership that draws on the scientific strengths of Canadian, Israeli and low and middle income countries (LMICs) researchers in the broad field of biomedicine. Aim: JCIHRP aims to advance research and discovery in the biomedical sciences; encourage scientific collaboration between Canadian and Israeli researchers; and build capacity and foster scientific relations and collaborations with researchers and trainees in LMICs. Methods: JCIHRP will fund up to 30 research projects from 2015 to 2022 in diverse areas of the biomedical sciences (neurosciences, immunology, cancer and metabolism). So far, the program is supporting 9 projects in cancer research. Teams are led by a Canadian and Israeli principal investigators and a collaborator from a LMIC. Three years is the maximum duration of each grant and teams can request up to CA$1.17 million. The program launches 1 competition each year and activities are coordinated by a directors working group, which is responsible for program implementation and coordination among the agencies. Annual implementation timeline can be divided into 4 phases: competition development and application; proposals' eligibility, selection and decision; research phase; and reporting and monitoring. In deploying these phases, the funding partners have shared effort and costs. Results: Among cancer research projects, 4 teams are developing strategies to improve effectiveness of cancer immunotherapy. Five other teams use advanced genomics and protein engineering techniques to elucidate molecular mechanisms associated with tumor development, progression and resistance to therapy in pancreatic, breast, hepatic and brain cancer. These projects are supporting 26 established researchers in 7 Canadian, 6 Israeli and 9 institutions based in Brazil, Mexico, China, India, Argentina and Turkey. Additionally, 19 graduate students and 9 postdoctoral fellows are directly involved in research activities. Type of collaboration can be grouped into 2 categories: research and training (5 projects) and research, training and exchange (4 projects). Conclusion: JCIHRP multicentre funding model allows international integration of researchers promoting scientific advances, new collaborations and enhancing teams' overall competitiveness by prioritizing research topics with potential for global impact in cancer research.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".