Awareness regarding antimicrobial resistance and confidence to prescribe antibiotics in dentistry: a cross-continental student survey
Bibliographic record
Abstract
BACKGROUND: The antimicrobial resistance (AMR) crisis is a major global threat and one of its biggest drivers is the overuse of antibiotics in humans. Dentists are responsible for 5-10% antibiotic prescriptions worldwide and recent data suggest that knowledge and prescribing practices need improvement. METHODS: A cross-sectional web-survey was sent to dental students from six universities in Norway, Canada, and Brazil. Topics addressed covered awareness, confidence to prescribe antibiotics, and education needs. Data were presented descriptively and statistical testing was employed to compare group means when applicable. RESULTS: In total, 562 responses were collected across the three countries with a response rate of 28.6%. 'Antibiotic resistance' was among the highest priorities (scale 1-10) with an average of 8.86 (SEM ± 0.05), together with 'Gender inequality' (8.68 ± 0.07) and 'Climate change' (8.68 ± 0.07). Only 28.8% thought that Dentistry was engaged in national/international campaigns promoting awareness on the topic and 8.9% stated to have heard about the 'One Health' concept. Final year dental students showed an average confidence to prescribe antibiotics of 7.59 (± 0.14). Most students demonstrated interest in receiving additional education on all topics listed, with the three most pressing being 'antibiotic prescription for treatment of infections' (82.9%), 'drug interactions' (80.9%), and 'spread of antibiotic resistance' (79.6%). A trend was observed between higher awareness regarding the topic and higher confidence to prescribe. CONCLUSIONS: There is a need to revisit dental education on antibiotic resistance with a global perspective and to create more stewardship initiatives that promote awareness on the topic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".