Knowledge and practices of poultry workers on prevention of avian flu in osogbo, osun state, Nigeria.
Bibliographic record
Abstract
INTRODUCTION: Nigeria recorded its first case of avian flu among birds early in the year 2006 and by the third quarter of the year, about sixteen states of the country had been affected. Finding out the knowledge and practices of persons in close contact with birds as regards the avian flu would help to identify areas in need of focused attention and alert the coordinating agencies of the magnitude and prevalence of practices which may encourage the spread of the disease. METHODOLOGY: This was a descriptive cross-sectional survey of poultry owners and workers in Osogbo, Osun State, southwestern Nigeria. Information was obtained from 65 of the 100 registered members of the poultry association who consented to answering questions on the pre-tested semi-structured questionnaires. RESULT: In this study, 49.1% (28) of the study sample knew of avian flu, and 46.4% (13) of these knew that the disease was present in Nigeria. Only 50% (14) of those who knew about the disease felt it could affect human beings. For those who have heard of the disease before, 85.7% (24) knew it could be transmitted from sick birds to humans. Practices which favour the spread of the virus that were engaged in by the respondents included using bird droppings as manure (71.9%), and feeding fish with intestines of killed chicken (66.7%). CONCLUSION: The study shows a low level of awareness of avian flu among the poultry workers and owners. It is recommended that massive education should be embarked upon for groups occupying strategic positions in disease epidemiology, as well as all stake holders in poultry farming.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".