Insights Into Antimicrobial Resistance From Dental Students in the Asia–Pacific Region
Bibliographic record
Abstract
BACKGROUND: Dentists, as prominent prescribers, are key stakeholders in addressing the antimicrobial resistance (AMR) crisis. Dental students' perceptions about the topic have been underexplored in the Asia-Pacific region, a key location for the development and spread of AMR. Thus, the aim of this study was to evaluate the awareness and confidence to prescribe antimicrobials amongst dental students studying in the region. METHODS: Students from 15 dental schools in 4 countries were invited to participate in a cross-sectional online survey during 2022-2023. A previously validated and standardised 14-item instrument was utilised. RESULTS: In all, 1413 responses were collected from Australia (n = 165), Sri Lanka (n = 112), Japan (n = 173), and Vietnam (n = 963). Of those, 201 were from final-year students (14.2%). On a scale from 1 to 10, awareness on AMR was placed at a mean (SEM) priority of 8.09 (0.05). With regards to target areas to address for mitigation of the AMR crisis, participants placed general public awareness at the top (mean [SEM] 8.53 [0.05]). Final-year students presented a mean (SEM) level of confidence to prescribe antibiotics of 6.01 (0.14) on a scale from 1 to 10, whilst 59.7% and 56.8% indicated feeling pressured to prescribe by patients or when lacking time, respectively. Final-year students participating in research activities assigned a higher priority to AMR compared to their peers not involved in research (mean [SEM] 8.6 [0.19] vs 7.81 [0.16]; P = .01). CONCLUSIONS: This study highlights a need for increased awareness and confidence to prescribe amongst dental students in the Asia-Pacific region, an understudied population thus far. To mitigate this issue, the implementation (followed by assessment) of local educational and antibiotic stewardship initiatives is warranted.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".