Factors influencing the performance of community health volunteers working within urban informal settlements in low- and middle-income countries: a qualitative meta-synthesis review
Bibliographic record
Abstract
BACKGROUND: There is limited information on community health volunteer (CHV) programmes in urban informal settlements in low- and middle-income countries (LMICs). This is despite such settings accounting for a high burden of disease. Many factors intersect to influence the performance of CHVs working in urban informal settlements in LMICs. This review was conducted to identify both the programme level and contextual factors influencing performance of CHVs working in urban informal settlements in LMICs. METHODS: Four databases were searched for qualitative and mixed method studies focusing on CHVs working in urban and peri-urban informal settlements in LMICs. We focused on CHV programme outcome measures at CHV individual level. A total of 13 studies met the inclusion criteria and were double read to extract relevant data. Thematic coding was conducted, and data synthesized across ten categories of both programme and contextual factors influencing CHV performance. Quality was assessed using both the Critical Appraisal Skills Programme (CASP) and the Mixed Methods Assessment Tool (MMAST); and certainty of evidence evaluated using the Confidence in the Evidence from Reviews of Qualitative research (CERQual) approach. RESULTS: Key programme-level factors reported to enhance CHV performance in urban informal settlements in LMICs included both financial and non-financial incentives, training, the availability of supplies and resources, health system linkage, family support, and supportive supervision. At the broad contextual level, factors found to negatively influence the performance of CHVs included insecurity in terms of personal safety and the demand for financial and material support by households within the community. These factors interacted to shape CHV performance and impacted on implementation of CHV programmes in urban informal settlements. CONCLUSION: This review identified the influence of both programme-level and contextual factors on CHVs working in both urban and peri-urban informal settlements in LMICs. The findings suggest that programmes working in such settings should consider adequate remuneration for CHVs, integrated and holistic training, adequate supplies and resources, adequate health system linkages, family support and supportive supervision. In addition, programmes should also consider CHV personal safety issues and the community expectations.
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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 | 0.035 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".