Young doctors’ perspectives on antibiotic use and resistance: a multinational and inter-specialty cross-sectional European Society of Clinical Microbiology and Infectious Diseases (ESCMID) survey
Bibliographic record
Abstract
BACKGROUND: Postgraduate training has the potential to shape the prescribing practices of young doctors. OBJECTIVES: To investigate the practices, attitudes and beliefs on antibiotic use and resistance in young doctors of different specialties. METHODS: We performed an international web-based exploratory survey. Principal component analysis (PCA) and bivariate and multivariate [analysis of variance (ANOVA)] analyses were used to investigate differences between young doctors according to their country of specialization, specialty, year of training and gender. RESULTS: Of the 2366 participants from France, Greece, Italy, Portugal, Slovenia and Spain, 54.2% of young doctors prescribed antibiotics predominantly as instructed by a mentor. Associations between the variability of answers and the country of training were observed across most questions, followed by variability according to the specialty. Very few differences were associated with the year of training and gender. PCA revealed five dimensions of antibiotic prescribing culture: self-assessment of knowledge, consideration of side effects, perception of prescription patterns, consideration of patient sickness and perception of antibiotic resistance. Only the country of specialization (partial η2 0.010-0.111) and the type of specialization (0.013-0.032) had a significant effect on all five identified dimensions (P < 0.01). The strongest effects were observed on self-assessed knowledge and in the perception of antibiotic resistance. CONCLUSIONS: The country of specialization followed by the type of specialization are the most important determinants of young doctors' perspectives on antibiotic use and resistance. The inclusion of competencies in antibiotic use in all specialty curricula and international harmonization of training should be considered.
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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 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".