Setting an implementation research agenda for Canadian investments in global maternal, newborn, child and adolescent health: a research prioritization exercise
Bibliographic record
Abstract
BACKGROUND: Improving global maternal, newborn, child and adolescent health (MNCAH) is a top development priority in Canada, as shown by the $6.35 billion in pledges toward the Muskoka Initiative since 2010. To guide Canadian research investments, we aimed to systematically identify a set of implementation research priorities for MNCAH in low- and middle-income countries. METHODS: We adapted the Child Health and Nutrition Research Initiative method. We scanned the Child Health and Nutrition Research Initiative literature and extracted research questions pertaining to delivery of interventions, inviting Canadian experts on MNCAH to generate additional questions. The experts scored a combined list of 97 questions against 5 criteria: answerability, feasibility, deliverability, impact and effect on equity. These questions were ranked using a research priority score, and the average expert agreement score was calculated for each question. RESULTS: The overall research priority score ranged from 40.14 to 89.25, with a median of 71.84. The average expert agreement scores ranged from 0.51 to 0.82, with a median of 0.64. Highly-ranked research questions varied across the life course and focused on improving detection and care-seeking for childhood illnesses, overcoming barriers to intervention uptake and delivery, effectively implementing human resources and mobile technology, and increasing coverage among at-risk populations. Children were the most represented target population and most questions pertained to interventions delivered at the household or community level. INTERPRETATION: Investing in implementation research is critical to achieving the Sustainable Development Goal of ensuring health and well-being for all. The proposed research agenda is expected to drive action and Canadian research investments to improve MNCAH.
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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 | 0.296 | 0.264 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.031 | 0.012 |
| Scholarly communication | 0.029 | 0.013 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".