Antibiotic prescribing and antimicrobial resistance awareness among medical students in Lebanon using novel assessment scales
Bibliographic record
Abstract
In Lebanon, widespread antibiotic prescribing has led to antimicrobial resistance (AMR), a notable public health concern driven by inappropriate use. As future prescribers, medical students play a critical role in combating AMR. This study aimed to evaluate their AMR awareness and antibiotic prescribing practices, identify knowledge gaps, and suggest interventions to promote appropriate antibiotic use and limit local AMR emergence. A cross-sectional study was conducted among Lebanese medical students from July 1, 2021, to September 30, 2022. Data were collected via a structured online questionnaire using snowball sampling. The questionnaire was informed by published articles and the authors' expertise. It included sociodemographic data and novel, context-specific scales assessing antibiotic prescribing attitudes, knowledge of AMR drivers, and prudent prescribing practices, addressing gaps in existing tools. The sample included 164 medical students, with a mean age of 22.78 ± 3.94 years. Furthermore, 89.6% were interns, 50.6% were females, and 68.3% studied at a public university. Antibiotic prescription attitudes among the surveyed participants were affected by a better awareness of some contextual factors related to the prescribed drug and the pharmaceutical companies. Decreased awareness was observed for pharmacist-related factors, patient characteristics, and marketing-related factors. Interns had higher awareness than residents about marketing and the contextual factors influencing antibiotic prescribing. Participants had average knowledge levels of AMR and relatively high knowledge levels of strategies to manage it. A significant interrelation was found between awareness of factors influencing physicians' antibiotic-prescribing attitudes and AMR knowledge (p < 0.05). Lebanese medical students demonstrated adequate overall knowledge of antibiotic use and resistance, along with generally positive attitudes toward appropriate antibiotic prescribing. However, gaps were noted in their understanding of prudent antibiotic prescribing practices. Targeted interventions, including curriculum enhancement, AMR-focused workshops, stewardship training, and supportive national policies, are recommended to strengthen AMR awareness and promote responsible antibiotic use.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".