Community knowledge, attitude and practices regarding zoonotic viral haemorrhagic fevers in five geo-ecological zones in Tanzania
Bibliographic record
Abstract
Abstract Background Viral haemorrhagic fevers (VHF) cause significant economic and public health impact in Sub-Saharan Africa. Community knowledge, awareness and practices regarding such outbreaks play a pivotal role in their management and prevention. This study was carried out to assess community knowledge, attitude and practices regarding VHF in five geo-ecological zones in Tanzania. Methods A cross-sectional study was conducted in Buhigwe, Kalambo, Kyela, Kinondoni, Kilindi, Mvomero, Kondoa and Ukerewe districts representing five geo-ecological zones in Tanzania. Study participants were selected by multistage cluster sampling design. A semi-structured questionnaire was used to collect socio-demographic and information related to knowledge, attitude and practices regarding VHFs. Descriptive statistics and logistic regression were used for the analysis. Results A total of 2,965 individuals were involved in the study. Their mean age was 35 (SD ± 18.9) years. Females accounted for 58.2% while males 41.8%. Most of the respondents (70.6%; n = 2093) had never heard of VHF, and those who heard, over three quarters (79%) mentioned the radio as their primary source of information. Slightly over a quarter (29.4%) of the respondents were knowledgeable, 25% had a positive attitude, and 17.9% had unfavourable practice habits. The level of knowledge varied between occupation and education levels (P < 0.005). Most participants were likely to interact with a VHF survivor or take care of a person suffering from VHF (75%) or visit areas with known VHF (73%). There were increased odds of having poor practice among participants aged 36–45 years (AOR: 3.566, 95% CI: 1.593–7.821) and those living in Western, North-Eastern and Lake Victoria zones (AOR: 2.529, 95% CI: 1.071–6.657; AOR: 2.639, 95% CI: 1.130–7.580 AOR: 2.248, 95% CI: 1.073–3.844, respectively). Conclusion Overall, the knowledge on VHF among communities is low, while a large proportion of individuals in the community are involved in activities that expose them to the disease pathogens in Tanzania. These findings highlight the need for strengthening health educational and promotion efforts on VHF targeting specific populations.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".