Developing collaborative approaches to international research: Perspectives of new global health researchers
Bibliographic record
Abstract
Within a global context of growing health inequities, the fostering of partnerships and collaborative research have been promoted as playing a critical role in tackling health inequities and health system problems worldwide. Since 2004, the Canadian Coalition for Global Health Research (CCGHR) has facilitated annual Summer Institutes for new global health researchers aimed at strengthening global health research competencies and partnerships among participants. We sought to explore CCGHR Summer Institute alumni perspectives on the Summer Institute experience, particularly on the individual research pairings of Canadian and low- and middle-income countries researchers that have characterised the program. The results reveal that the Summer Institute offered an enriching learning opportunity for participants and worked to further their collaborative projects through providing dedicated one-on-one time with their international research partner, feedback from colleagues from around the world and mentorship by more senior researchers. Positive individual relationships among researchers, as well as the existence of institutional collaborations, employer and funding support, and agendas of local and national politicians were factors that have influenced the ongoing collaboration of partners. There is a need to more fully examine the interplay between individual and institutional-level collaborations, as well as their social and political contexts.
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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 | 0.152 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.032 | 0.069 |
| Scholarly communication | 0.052 | 0.034 |
| Open science | 0.006 | 0.038 |
| Research integrity | 0.015 | 0.025 |
| 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".