How To Prepare Dental Students For The Antibiotic Resistance Crisis?
Bibliographic record
Abstract
Antibiotic resistance is one of the most serious issues facing global societies. Millions of people die yearly due to infections that are not responsive to antibiotic treatment strategies anymore. As active prescribers of antibiotics, the dental profession plays a central role in mitigating the progression of antibiotic resistance. Therefore, the aim of this project was to approach dental students in the Asia Pacific region to understand the challenges they currently face when dealing with antibiotic resistance. A semi-structured questionnaire with both close and open-ended questions was employed digitally to collect responses from dental students in all years from institutions in four different countries in the Asia Pacific region. Recurring and concerning themes were identified in open-ended responses with regards to dissonance between the theoretical knowledge and clinical application, as well as a large variation regarding education received from instructors. Students highlighted that the pressing issue of microbiology and antibiotic resistance is often addressed in the first years of dental education, with little time committed to it once clinical training commences. Further, the added pressure from patients to have antibiotics prescribed was noted both in close as well as in open-ended responses. Microbiology and antibiotic resistance occupy a unique position in oral health care due to their involvement in the etiopathology of oral diseases that present high burden and impact worldwide. The data presented here coupled with mounting evidence in the literature clearly indicates that the theme needs to be better embedded and emphasized in dental curricula.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".