A cross-sectional survey of avian influenza knowledge among poultry farmworkers in Indonesia
Bibliographic record
Abstract
Background: Avian influenza (AI) poses a serious threat to global public health, especially the highly pathogenic form. Awareness and protective behavior among the public, particularly the high-risk populations, are essential for prevention and control. This study aimed to ascertain the level of AI knowledge among Indonesia's poultry farmworkers. Methods: WhatsApp and email platforms. Volunteers (respondents) included 119 men and 81 women, aged 18-50 years, who work on poultry farms in Indonesia. Data were analyzed using the chi-squared and Fisher exact tests. Results: The study's findings revealed that more than two-thirds (67.0%) of the respondents had heard about AI. Their primary sources of information were health workers (36.0%) and media, especially television (34.0%). The majority of the participants (91.3%) had good knowledge about AI as a contagious infection, transmissible from birds to other birds, animals, or humans. A total of 76.8% of the respondents believed that poultry workers and veterinarians were at high risk of contracting AI infection. Conclusions: The study concluded that poultry workers had good knowledge about AI infection, transmission, and risk variables. Health workers and television were the main sources of information on AI. The level of AI knowledge was high among the respondents.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".