A scoping review of the roles, challenges, and strategies for enhancing the performance of community health workers in the response against COVID-19 in low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Global concerns regarding effective response strategies to the COVID-19 pandemic arose amid the swift spread of the virus to low- and middle-income country (LMIC) settings. Although LMICs instituted several measures to mitigate spread of the virus in low resource settings, including task shifting certain demand and supply functions to community actors such as community health workers (CHWs), there remains a lack of synthesized evidence on these experiences and lessons. This scoping review sought to synthesize evidence regarding the roles and challenges faced by CHWs during the fight against COVID-19, along with strategies to address these challenges. METHODOLOGY: We systematically searched several major electronic databases including PubMed, HINARI, Cochrane Library (Reviews and Trials), Science Direct and Google Scholar for relevant literature. The search strategy was designed to capture literature published in LMICs on CHWs roles during COVID-19 period spanning 2019-2023. Two researchers were responsible for retrieving these studies, and critically reviewed them in accordance with Arksey and O'Malley scoping review approach. In total, 22 articles were included and analysed using Clarke and Braun thematic analysis in NVivo 12 Pro Software. RESULTS: Community health workers (CHWs) played a vital role during the COVID-19 pandemic. They engaged in health promotion and education, conducted surveillance and contact tracing, supported quarantine efforts, and maintained essential primary health services. They also facilitated referrals, advocated for clients and communities, and contributed to vaccination planning and coordination, including tracking and follow-up. However, CHWs faced significant challenges, including a lack of supplies, inadequate infection prevention and control measures, and stigma from community members. Additionally, they encountered limited supportive policies, insufficient remuneration and incentives. To enhance CHWs' performance, regular training on preventive measures is essential. Utilizing digital technology, such as mobile health, can be beneficial. Establishing collaborative groups through messaging platforms and prioritizing access to COVID-19 vaccines are important steps. Additionally, delivering wellness programs and providing quality protective equipment for CHWs are crucial for their effectiveness. CONCLUSION: The study found that CHWs are vital actors within the health system during global pandemics like COVID-19. This entails the need for increased support and investment to better integrate CHWs into health systems during such crises, which could ultimately contribute to sustaining the credibility of CHWs programs and foster more inclusive community health systems (CHSs).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".