The way forward for integrated community case management programmes: A summary of lessons learned to date and future priorities
Bibliographic record
Abstract
Integrated community case management (iCCM) programming is an important and increasingly common strategy used to deliver essential health and nutrition interventions to families in sub-Saharan Africa [1-3].Between 3 and 5 March 2014, over 400 individuals from 35 countries in sub-Saharan Africa and 59 international partner organisations gathered in Accra, Ghana for an iCCM Evidence Review Symposium.The objective of the Symposium was twofold: first, to review the current state of the art of iCCM implementation by bringing together researchers, donors, government, implementers and partners to review the map of the current landscape and status of evidence in key iCCM programme areas, in order to draw out priorities, lessons and gaps for improving child and maternal-newborn health and nutrition.Second, to assist African countries to integrate and take action on key frontline iCCM findings presented during the evidence Symposium around eight thematic areas: 1) Coordination, Policy Setting and Scale up; 2) Human Resources and Deployment; 3) Supervision & Performance Quality Assurance; 4) Supply Chain Management; 5) Costs, and cost-effectiveness and financing; 6) Monitoring, Evaluation and Health Information Systems; 7) Demand generation and social mobilisation; and
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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 | 0.053 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".