Health for the People: Past, Current, and Future Contributions of National Community Health Worker Programs to Achieving Global Health Goals
Bibliographic record
Abstract
Key Messages After almost a century of experience, innovation, adaptation, and evidence, national community health worker (CHW) programs are now recognized as one of the most valuable assets for reaching global health goals, including achieving universal health coverage and ending preventable child and maternal deaths by 2030. In 2019, the United Nations General Assembly called urgently to accelerate progress in achieving these global health goals recognizing that, at the current pace, these goals will not be achieved for up to one-third of the world9s population. There is rapidly growing interest not only in CHWs but in community health more broadly, in engagement with communities for improving their own health, and in community-based surveillance for infectious disease outbreaks, especially now that the world is struggling to combat COVID-19 and is likely to face similar pandemics in the future. Training more professionalized CHWs with better and longer training, better supervision, improved logistical support, and well-defined career paths, and linking them to lower-level volunteer workers, each serving a small number of households, will help strengthen program effectiveness and improve CHW morale and long-term retention.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".