Evaluating the Role of Veterinarians in the One Health Approach to Antimicrobial Resistance in Jordan
Bibliographic record
Abstract
Background Antimicrobials, including antibiotics, antivirals, antifungals, and antiparasitics, are drugs used to prevent and treat infections in humans, animals, and plants. Objective This study aimed to evaluate the role of knowledge, attitudes, and practices of Jordanian veterinarians in combating antimicrobial resistance (AMR), and to summarize the registered veterinary drugs between 2017 and 2020. Methods The descriptive study data were collected using a standardized questionnaire focusing on knowledge, attitudes, and practices of Jordanian veterinarians. Results The results were analyzed descriptively and showed that the mean knowledge of the participants who agreed with the statement on AMR definition was 84%. The majority (95.65%) agreed that AMR is a challenge for the veterinary sector in Jordan and should be prioritized among other zoonotic diseases. Around 69% of the participants believe that the misuse and overuse of antimicrobials by quacks—fraudulent and unauthorized practitioners—are the main reasons for AMR challenge. The most common practice among the respondents was recommending clients (farmers, owners, etc) to practice good animal husbandry (80%). The study also revealed that there was a significant difference (P=.02) between attending training about AMR and their professional sector (private, public, and academic). Conclusions This study showed the importance of implementing a continuous education program on antimicrobial resistance to improve veterinarians’ knowledge in all aspects of antimicrobial resistance and to increase their advisory skills. Laws should also be enacted to ensure that veterinarians prescribe the correct antimicrobials and improve the surveillance system to monitor the use of antimicrobials in veterinary medicine.
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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 | 0.007 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".