Health workers’ knowledge of zoonotic diseases in an endemic region of Western Uganda
Bibliographic record
Abstract
Many factors, including lack of knowledge, influence diagnosis and reporting of disease in Sub-Saharan Africa. Health Care workers (HCWs) are in constant interaction with communities and play an important role in the prevention, diagnosis and treatment of infectious diseases, including zoonoses. We determined knowledge of HCWs regarding cause, vector, transmission, diagnosis and clinical symptoms of five zoonotic diseases: anthrax, brucellosis, rabies as well as Ebola and marburg haemorrhagic fevers in endemic western Uganda. This was a descriptive cross-sectional study among HCWs based at health centres in and around Queen Elizabeth Conservation Area, Western Uganda. A self-administered questionnaire was used to measure knowledge of these five most common zoonoses recently recorded in the area. Data were captured as true if the responses were correct or false if incorrect. Analyses were in STATA and inferential statistics by cross-tabulation, and a chi-square P-value of less than 0.05 was considered significant. A majority (114/140; 81.4%) of the respondents had heard about zoonoses. The most accurately identified zoonoses were anthrax (128/140; 91.4%) closely followed by rabies (126/140; 90%), while only 21 (15%) respondents knew that cryptosporidiosis was zoonotic. Up to 20% (28/140) and 12.8% (18/140) thought that malaria and HIV, respectively, were zoonotic. There was poor overall knowledge of the endemic diseases brucellosis among all the participants, where only 1.4% (2/140) knew its causative agent, clinical symptoms and transmission. There was a total lack of knowledge (0%) about anthrax and Ebola whereby none of the 140 HCWs knew all the three above aspects required to be knowledgeable for each of the two diseases. Generally, there was poor knowledge of the five zoonoses. We recommend that medical curricula incorporate training on zoonotic and other emerging diseases, and continuing medical education regarding zoonoses should be designed for the HCWs practicing in hotspot zones.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".