Setting global research priorities for integrated community case management (iCCM): Results from a CHNRI (Child Health and Nutrition Research Initiative) exercise
Bibliographic record
Abstract
AIMS: To systematically identify global research gaps and resource priorities for integrated community case management (iCCM). METHODS: An iCCM Child Health and Nutrition Research Initiative (CHNRI) Advisory Group, in collaboration with the Community Case Management Operational Research Group (CCM ORG) identified experts to participate in a CHNRI research priority setting exercise. These experts generated and systematically ranked research questions for iCCM. Research questions were ranked using a "Research Priority Score" (RPS) and the "Average Expert Agreement" (AEA) was calculated for every question. Our groups of experts were comprised of both individuals working in Ministries of Health or Non Governmental Organizations (NGOs) in low- and middle-income countries (LMICs) and individuals working in high-income countries (HICs) in academia or NGO headquarters. A Spearman's Rho was calculated to determine the correlation between the two groups' research questions' ranks. RESULTS: The overall RPS ranged from 64.58 to 89.31, with a median score of 81.43. AEA scores ranged from 0.54 to 0.86. Research questions involving increasing the uptake of iCCM services, research questions concerning the motivation, retention, training and supervision of Community Health Workers (CHWs) and concerning adding additional responsibilities including counselling for infant and young child feeding (IYCF) and treatment of severe acute malnutrition (SAM) ranked highly. There was weak to moderate, statistically significant, correlation between scores by representatives of high-income countries and those working in-country or regionally (Spearman's ρ = 0.35034, P < 0.01). CONCLUSIONS: Operational research to determine optimal training, supervision and modes of motivation and retention for the CHW is vital for improving iCCM, globally, as is research to motivate caregivers to take advantage of iCCM services. Experts working in-country or regionally in LMICs prioritized different research questions than those working in organization headquarters in HICs. Further exploration is needed to determine the nature of this divergence.
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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 | 0.292 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| 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; 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".