KNOWLEDGE AND ATTITUDE OF FARMERS TOWARDS ANTIMICROBIAL RESISTANCE IN ASIA: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Background: Antimicrobial resistance is a severe threat to public and environmental health. The agricultural sector contributes significantly to resistance, where antimicrobials are used as prophylaxis, growth promoters, and for treatment. A series of studies have been conducted to assess farmers' knowledge and attitude levels with varying results, particularly in Asia, one of the world's largest producers of livestock products. Purpose: To review the pooled estimated level of knowledge and attitude towards antimicrobial use and resistance in Asia. Methods: A literature search was conducted according to PRISMA in Scopus, PubMed, Google Scholar, and Embase for studies up to 30 April 2023. Quality was assessed using the Newcastle-Ottawa Scale (NOS) for cross-sectional studies. Outcomes were further categorized into constructs under knowledge and attitude. Random-effect meta-analysis was conducted using STATA 17. Results: 11 studies and 2131 subjects were included with fair to excellent quality. From the meta-analysis, the following knowledge and attitude levels were estimated: definition [55.7% (95%CI: 37.3%-74%)] and cause [60.6% (95%CI: 40.5%-80.6%)] of antimicrobial resistance; the negative impact of antimicrobials [62.6% (95%CI: 16.9%-100.0%)]; use of antimicrobials for treatment [47.8% (95%CI: 6.1%-89. 4%)], prophylaxis [58.5% (95%CI: 28.5%-88.5%)], growth promoter [39% (95%CI: 23.1%-54.9%)]; discontinuation of antimicrobials upon improving conditions [42.5% (95%CI: 15.4%-69.5%)]. Conclusions: Farmers in Asia have moderate knowledge of antimicrobial resistance but still exhibit attitudes that support resistance.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".