Poor retention does not have to be the rule: retention of volunteer community health workers in Uganda
Bibliographic record
Abstract
Globally, health worker shortages continue to plague developing countries. Community health workers are increasingly being promoted to extend primary health care to underserved populations. Since 2004, Healthy Child Uganda (HCU) has trained volunteer community health workers in child health promotion in rural southwest Uganda. This study analyses the retention and motivation of volunteer community health workers trained by HCU. It presents retention rates over a 5-year period and provides insight into volunteer motivation. The findings are based on a 2010 retrospective review of the community health worker registry and the results of a survey on selection and motivation. The survey was comprised of qualitative and quantitative questions and verbally administered to a convenience sample of project participants. Between February 2004 and July 2009, HCU trained 404 community health workers (69% female) in 175 villages. Volunteers had an average age of 36.7 years, 4.9 children and some primary school education. Ninety-six per cent of volunteer community health workers were retained after 1 year (389/404), 91% after 2 years (386/404) and 86% after 5 years (101/117). Of the 54 'dropouts', main reasons cited for discontinuation included 'too busy' (12), moved (11), business/employment (8), death (6) and separation/divorce (6). Of 58 questionnaire respondents, most (87%) reported having been selected at an inclusive community meeting. Pair-wise ranking was used to assess the importance of seven 'motivational factors' among respondents. Those highest ranked were 'improved child health', 'education/training' and 'being asked for advice/assistance by peers', while the modest 'transport allowance' ranked lowest. Our findings suggest that in our rural, African setting, volunteer community health workers can be retained over the medium term. Community health worker programmes should invest in community involvement in selection, quality training, supportive supervision and incentives, which may promote improved retention.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".