Engaging Community Health Workers (CHWs) in Africa: Lessons from the Canadian Red Cross supported programs
Bibliographic record
Abstract
Universal Health Coverage (UHC) will not be achieved if health care worker shortages, estimated to increase to 18 million by 2030, are not addressed rapidly. Community-based health systems, which pivot to effective engagement of community health workers (CHW), may have an essential role in linking communities with health care facilities and reducing unmet health services needs caused by these shortages. The Canadian Red Cross (CRC) has partnered with different National Red Cross/Red Crescent Societies and Ministries of Health in Africa in the implementation of programs where CHWs contributed to the provision of various health services. This study reports on key findings (i.e., beneficiaries reached, CHWs engaged, programs implemented, intervention outcomes) and lessons learned from CRC supported CHW programs in Africa over the last 15 years (2007-2022). Qualitative methodology was employed to conduct document analysis on 17 sets of reports from each CRC-supported community health worker project in Africa over the past 15 years. Focus was on identifying challenges, facilitators, and lessons learned. CRC supported projects have trained over 9000 CHWs, benefiting nearly 7.5 million people across Africa. Key success factors include adaptability and agility in programming and project management, and considering contextual factors (political, social, and cultural systems). Investing in essential training for CHWs, staff, and volunteers is crucial, alongside employing an evidence-based approach to inform all aspects of programming and implementation. Additionally, projects prioritizing protection, gender and inclusion (PGI) while leveraging existing community structures and partnerships important for successful implementation. Despite challenges (i.e., weak health systems, lack of political commitment, insufficient funding, inadequate training) CHWs are recognized as crucial in promoting community-based health, improving access to care, addressing disparities, and contributing to achieving (UHC). Their unique position within communities enables them to provide culturally appropriate and localized primary health care- particularly in remote, resource limited and poverty-stricken regions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".