African leadership and international collaboration to address global health challenges: Learnings from the Innovating for Maternal and Child Health in Africa (IMCHA) initiative
Bibliographic record
Abstract
Cooperation is understood to be key for improving global health outcomes. Authentic partnering in global health 1 research nurtures collaboration that addresses health problems, and expands scientific knowledge that brings benefits to all parties. The Government of Canada supports collaboration between Canadian and developing country researchers and decision-makers in providing solutions to key challenges related to women’s, newborns’, children’s, and adolescents’ health, through interdisciplinary, innovative, collaborative, and impactful research. In March 2014, the Innovating for Maternal and Child Health in Africa (IMCHA) Initiative was launched as a contribution to Canada’s commitments to maternal, neonatal and child health at the 2010 G8 Summit in Muskoka, Canada 2 . IMCHA was jointly funded by the International Development Research Centre (IDRC), the Canadian Institutes of Health Research (CIHR) and Global Affairs Canada (GAC). This CAD $36 million initiative brought together African researchers, Canadian researchers, and African decision-makers, who are the users of the implementation research evidence, for increased impact and potential for sustainability and scale. The Initiative is ending in July 2021, for a total duration of almost 8 years. The specific objectives of IMCHA were to: Address critical knowledge gaps and increase awareness among policy decision-makers about affordable, feasible, and scalable primary health care interventions to improve maternal and child health delivery and outcomes; Build individual and institutional capacity for gender-sensitive health systems and solution-oriented research, and enhance the uptake of relevant and timely research that informs policy and practice; and Strengthen collaborations between Canadian and African researchers, working in partnership with African decision-makers, to implement and scale up high-quality and effective services and technologies that improve maternal and child health outcomes.
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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 | 0.050 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".