Innovative Approaches to Improve Knowledge of Zoonoses among Wildlife Hunters and Traders in Epe, Lagos, Nigeria: A Community Action Network-Based Intervention
Bibliographic record
Abstract
Abstract The West Africa One Health project is a multi-country project designed to utilise the One Health approach and deploy the Community Action Networks (CAN) to improve knowledge of high-risk communities on zoonoses. Majority of emerging zoonoses occur at the human-wildlife interface, of which wildlife hunters and traders are critical stakeholders. We assessed the effectiveness of a CAN-based intervention involving the use of a video documentary and case studies as model tools in improving the knowledge of zoonoses among wildlife hunters and traders in Epe, an established hunting community in Lagos State, Nigeria. A quasi-experimental study design involving a total of 39 consenting registered wildlife stakeholders was adopted. A pre-tested, semi-structured, interviewer-administered questionnaire was used to obtain data on the participant’s sociodemographic characteristics, awareness level, and knowledge of zoonoses pre and post CAN-based intervention. Data were analysed using descriptive statistics McNemar and Wilcoxon Signed Ranks tests at a 5% level of significance. The mean age of the participants was 46.7 ± 10.9 years. Most (76.9%) identified as male and had at least secondary education (89.7%). The number of participants who were aware that diseases could be contracted from animals and that it could be through inhalation and close contact increased significantly from 13 (33.3%), 2 (5.1%), and 9 (23.1%) pre-intervention to 37 (94.9 %), 11 (28.2%), and 21 (53.8%) post-intervention, respectively. The overall median knowledge score increased significantly from 1 (Interquartile range (IQR): 0 – 2) pre-intervention to 3 (IQR: 2 – 4) post-intervention. The CAN-based intervention involving the use of a video documentary and case studies as model tools was effective in improving the knowledge of zoonoses among wildlife hunters and traders in the hunting community and may be beneficial for future practice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".