Survey on antimicrobial resistance knowledge and perceptions in university students reveals concerning trends on antibiotic use and procurement
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) has declared that antimicrobial resistance is one of the top ten global public health threats humanity is facing. To tackle this problem, it is necessary to not only address it in the hospital setting, but even more so in the community. In this context, understanding people's knowledge, attitudes, and practices towards antimicrobial resistance is of utmost importance. Accordingly, we investigated whether students from the Université de Montréal (Quebec, Canada) had perceptions and behaviours that could foster bacterial resistance. METHODS: We conducted an observational, cross-sectional, prospective, and descriptive study from November 30 to December 11, 2020. We applied an online questionnaire (Google Forms) adapted from the WHO survey entitled 'Antibiotic resistance: Multi-country public awareness survey.' RESULTS: Overall, 106 participants were included in this study. Most of them demonstrated reasonable understanding and behaviours related to antimicrobial resistance. Erroneous response proportions ranged from 0.9% to 25.5%, except for the statement 'Antibiotic resistance occurs when your body becomes resistant to antibiotics, and they no longer work,' where 63.2% of participants answered that it was true, even though it is false. Regarding antibiotic use, 28.3% of participants said they already had used antibiotics without a doctor's prescription. Of these, 55.2% were Canadian students. CONCLUSIONS: This study indicates a possible misuse of antimicrobials in an area where antibiotics should not be easily accessible without a prescription. It is necessary to investigate why these medications are being used without being prescribed. Furthermore, we demonstrate a need to increase public awareness to better understand antimicrobial resistance's theoretical basis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".