Perceptions, attitudes, and practices of French junior physicians regarding antibiotic use and resistance
Bibliographic record
Abstract
To assess the perceptions, attitudes, and practices of French junior physicians regarding antibiotic use and resistance, and then to identify the characteristics of junior physicians associated with appropriate practices of antibiotic use. European junior physicians received an email invitation to complete a 49 item web questionnaire between September 2015 and January 2016. We present the French data. Multivariate regression models were used to identify the characteristics of junior physicians associated with appropriate prescription practices and with consideration of the antibiotic prescription consequences. The questionnaire was completed by 641 junior physicians: family medicine (37%), other medical specialties (e.g., pediatrics, internal medicine, neurology: 45%), surgical specialties (11%), and anesthesiology-intensive care specialty (7%). Most respondents (93%) declared being aware of the risk of bacterial resistance and 41% acknowledged prescribing antibiotics more often than necessary. Two factors were independently associated with appropriate prescription practices: a high perceived level of education on antibiotic use (OR = 1.51; 95% CI [1.01–2.30]) and a medical specialty (OR = 1.69; 95% CI [1.16–2.46]). Factors independently associated with taking into account adverse events of antibiotics were a good perceived knowledge of antibiotics (OR = 3.71; 95% CI [2.09–6.61]), and a high perceived education level on antibiotics (OR = 1.70; 95% CI [1.11–2.58]). Our data can help better define interventions targeting junior physicians in antibiotic stewardship programs. Évaluer les perceptions, attitudes et pratiques des internes en médecine concernant les prescriptions antibiotiques et l’antibiorésistance. Identifier les facteurs associés aux bonnes pratiques de prescriptions antibiotiques. Les internes en médecine européens ont été invités par émail à remplir un questionnaire en ligne (49 items) entre septembre 2015 et janvier 2016. Nous présentons les données françaises. Des modèles de régression logistique multivariés ont été utilisés pour identifier les facteurs associés au suivi des recommandations de bon usage et à la prise en compte des effets secondaires des antibiotiques. Le questionnaire a été rempli par 641 internes : médecine générale (37 %), spécialités médicales (pédiatrie, médecine interne, neurologie : 45 %), chirurgicales (11 %) et anesthésie-réanimation (7 %). La majorité (93 %) disait être conscient du risque d’émergence de résistance bactérienne et 41 % affirmaient trop prescrire d’antibiotiques. Deux facteurs étaient associés aux bonnes pratiques de suivi des recommandations : un niveau perçu de formation élevé en antibiothérapie (OR = 1,51 ; IC 95 % [1,01–2,30]), l’exercice d’une spécialité médicale (OR = 1,69 ; IC 95 % [1,16–2,46]). Les facteurs associés à la prise en compte des effets secondaires des antibiotiques étaient un bon niveau déclaré de connaissances en antibiothérapie (OR = 3,71 ; IC 95 % [2,09–6,61]) et un bon niveau déclaré de formation en antibiothérapie (OR = 1,70 ; IC 95 % [1,11–2,58]). Ces données peuvent aider à mieux définir les interventions ciblant les internes dans les programmes de bon usage des antibiotiques.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".