Enhancing the capacity of community health workers in prevention and control of epidemics and pandemics in Wakiso district, Uganda: evaluation of a pilot project
Bibliographic record
Abstract
BACKGROUND: Community Health Workers (CHWs) play a crucial role in outbreak response, including health education, contact tracing, and referral of cases if adequately trained. A pilot project recently trained 766 CHWs in Wakiso district Uganda on epidemic and pandemic preparedness and response including COVID-19. This evaluation was carried out to generate evidence on the outcomes of the project that can inform preparations for future outbreaks in the country. METHODS: This was a qualitative evaluation carried out one year after the project. It used three data collection methods: 30 in-depth interviews among trained CHWs; 15 focus group discussions among community members served by CHWs; and 11 key informant interviews among community health stakeholders. The data was analysed using a thematic approach in NVivo (version 12). RESULTS: Findings from the study are presented under four themes. (1) Improved knowledge and skills on managing epidemics and pandemics. CHWs distinguished between the two terminologies and correctly identified the signs and symptoms of associated diseases. CHWs reported improved communication, treatment of illnesses, and report writing skills which were of great importance including for managing COVID-19 patients. (2) Enhanced attitudes towards managing epidemics and pandemics as CHWs showed dedication to their work and more confidence when performing tasks specifically health education on prevention measures for COVID-19. (3) Improved health practices such as hand washing, vaccination uptake, and wearing of masks in the community and amongst CHWs. (4) Enhanced performance in managing epidemics and pandemics which resulted in increased work efficiency of CHWs. CHWs were able to carry out community mobilization through door-to-door household visits and talks on community radios as part of the COVID-19 response. CHWs were also able to prioritize health services for the elderly, and support the management of patients with chronic diseases such as HIV, TB and diabetes by delivering their drugs. CONCLUSIONS: These findings demonstrate that CHWs can support epidemic and pandemic response when their capacity is enhanced. There is need to invest in routine training of CHWs to contribute to outbreak preparedness and response.
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 | 0.055 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".