Community health workers at the dawn of a new era: 11. CHWs leading the way to “Health for All”
Bibliographic record
Abstract
BACKGROUND: This is the concluding paper of our 11-paper supplement, "Community health workers at the dawn of a new era". METHODS: We relied on our collective experience, an extensive body of literature about community health workers (CHWs), and the other papers in this supplement to identify the most pressing challenges facing CHW programmes and approaches for strengthening CHW programmes. RESULTS: CHWs are increasingly being recognized as a critical resource for achieving national and global health goals. These goals include achieving the health-related Sustainable Development Goals of Universal Health Coverage, ending preventable child and maternal deaths, and making a major contribution to the control of HIV, tuberculosis, malaria, and noncommunicable diseases. CHWs can also play a critical role in responding to current and future pandemics. For these reasons, we argue that CHWs are now at the dawn of a new era. While CHW programmes have long been an underfunded afterthought, they are now front and centre as the emerging foundation of health systems. Despite this increased attention, CHW programmes continue to face the same pressing challenges: inadequate financing, lack of supplies and commodities, low compensation of CHWs, and inadequate supervision. We outline approaches for strengthening CHW programmes, arguing that their enormous potential will only be realized when investment and health system support matches rhetoric. Rigorous monitoring, evaluation, and implementation research are also needed to enable CHW programmes to continuously improve their quality and effectiveness. CONCLUSION: A marked increase in sustainable funding for CHW programmes is needed, and this will require increased domestic political support for prioritizing CHW programmes as economies grow and additional health-related funding becomes available. The paradigm shift called for here will be an important step in accelerating progress in achieving current global health goals and in reaching the goal of Health for All.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".