Ending preventable child deaths from pneumonia and diarrhoea by 2025. Development of the integrated Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea
Bibliographic record
Abstract
Despite the existence of low-cost and effective interventions for childhood pneumonia and diarrhoea, these conditions remain two of the leading killers of young children. Based on feedback from health professionals in countries with high child mortality, in 2009, WHO and Unicef began conceptualising an integrated approach for pneumonia and diarrhoea control. As part of this initiative, WHO and Unicef, with support from other partners, conducted a series of five workshops to facilitate the inclusion of coordinated actions for pneumonia and diarrhoea into the national health plans of 36 countries with high child mortality. This paper presents the findings from workshop and post-workshop follow-up activities and discusses the contribution of these findings to the development of the integrated Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea, which outlines the necessary actions for elimination of preventable child deaths from pneumonia and diarrhoea by 2025. Though this goal is ambitious, it is attainable through concerted efforts. By applying the lessons learned thus far and continuing to build upon them, and by leveraging existing political will and momentum for child survival, national governments and their supporting partners can ensure that preventable child deaths from pneumonia and diarrhoea are eventually eliminated.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".