Five-year retention of volunteer community health workers in rural Uganda: a population-based retrospective cohort
Bibliographic record
Abstract
Community health workers (CHWs) effectively improve maternal, newborn and child health (MNCH) outcomes in low-to-middle-income countries. However, CHW retention remains a challenge. This retrospective registry analysis evaluated medium-term retention of volunteer CHWs in two rural Ugandan districts, trained during a district-wide MNCH initiative. From 2012 to 2014, the Healthy Child Uganda partnership facilitated district-led CHW programme scale-up. CHW retention was tracked prospectively from the start of the intervention up to 2 years. Additional follow-up occurred at 5 years to confirm retention status. Database analysis assessed CHW demographic characteristics, retention rates and exit reasons 5 years post-intervention. A multivariable logistic regression model examined 5-year retention-associated characteristics. Of the original cohort of 2317 CHWs, 70% were female. The mean age was 38.8 years (standard deviation, SD: 10.0). Sixty months (5 years) after the start of the intervention, 84% of CHWs remained active. Of those exiting (n = 377), 63% reported a 'logistical' reason, such as relocation (n = 96), new job (n = 51) or death (n = 30). Sex [male, female; odds ratio (OR) = 1.53; 95% confidence interval (CI): 1 · 20-1 · 96] and age group (<25 years, 30-59; OR = 0.40; 95% CI: 0.25-0.62) were significantly associated with 5-year retention in multivariable modelling. Education completion (secondary school, primary) was not significantly associated with retention in adjusted analyses. CHWs in this relatively large cohort, trained and supervised within a national CHW programme and district-wide MNCH initiative, were retained over the medium term. Importantly, high 5-year retention in this intervention counters findings from other studies suggesting low retention in government-led and volunteer CHW programmes. Encouragingly, findings from our study suggest that retention was high, not significantly associated with timing of external partner support and largely not attributed to the CHW role i.e. workload and programme factors. Our study showcases the potential for sustainable volunteer CHW programming at scale and can inform planners and policymakers considering programme design, including selection and replacement planning for CHW networks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".