Perceptions of use and efficacy of antimicrobials by the public, farmers, medical and Veterinary professionals
Bibliographic record
Abstract
Background: Antibiotic resistance is now a global threat due to the misuse of antibiotics worldwide in both human and animals medicine. \n \nObjectives: The main aim of this research is to look at how antibiotics are used and perceived by different groups of individuals; Agriculture, veterinary, medical and the public in order to identify areas where more resources are required. \n \nSources of data: A questionnaire was circulated online for participants to complete anomalously. \n \nResults: The questionnaire was completed by a total of 874 participants globally with the majority from the UK. Results of the listed diseases were sub-divided in categories; viral, bacterial, pathogenic and syndromic. Of the viral diseases ‘foot and mouth disease’ showed the highest ‘Yes’ response with 17% (152) with the public being the highest groups. Both MRSA 23% (204) and Salmonella 22% (196) were the highest bacterial diseases that participants stated couldn’t not effectively be treated with antibiotics, in response to salmonella almost a quarter were from medical professionals. \n \nConclusion: Results from this questionnaire give an insight in to how antibiotics are used by different groups of individuals and their understanding of the development of resistance. This provides a platform to further develop specific areas that can be targeted. For example, education is a continual part of the process of reducing the uses of antibiotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".