Antibiotic Prescribing Behavior of Physicians in Outpatient Departments in Hospitals in Northwest Ethiopia: Structural Equation Modeling Approach
Notice bibliographique
Résumé
BACKGROUND: Antibiotic resistance, fueled by irrational prescribing, is a global threat associated with health, social, and economic consequences. Understanding antibiotic prescribing behavior and associated factors is important to promote good prescribing practice. OBJECTIVE: This study aimed to determine the factors affecting antibiotic prescribing behaviors of physicians based on the theory of planned behavior in hospitals in northwest Ethiopia in 2022. METHODS: A cross-sectional study was conducted from September 2022 to October 2022. A total of 185 health professionals were included, and a self-administered questionnaire was used to collect data. A structural equation model based on the modified theory of planned behavior was used to determine factors affecting antibiotic prescribing behavior. The percentages of physicians' estimated prescriptions for patients with upper respiratory tract infections (URTIs) and during weekly outpatient visits were used to predict antibiotic prescribing behavior and finally linked with behavioral constructs. A P value <.05 was considered significant. RESULTS: Physicians estimated that they prescribed antibiotics for 54.8% (9896/18,049) of weekly outpatient encounters, and 178 (96.2%) of the 185 physicians estimated they prescribed antibiotics for patients who presented with symptoms of a URTI. Physicians aged ≤30 years were less likely to prescribe antibiotics (48/100, 48%) for patients who presented with a URTI than physicians older than 30 years (51/100, 51%; P=.004), and general practitioners were less likely to prescribe antibiotics (47/100, 47%) for patients who presented with a URTI than residents (51/100, 51%; P=.03). Similarly, during outpatient visits, physicians ≤30 years old were less likely to prescribe antibiotics (54/100, 54%) than physicians older than 30 years (57/100, 57%; P<.001), male physicians were less likely to prescribe antibiotics (53/100, 53%) than female physicians (64/100, 64%; P=.03), and general practitioners were less likely to prescribe antibiotics (53/100, 53%) than residents (57/100, 57%; P=.02). Physicians with good knowledge were less affected by perceived social pressure (mean 4.4, SD 0.6) than those with poor knowledge (mean 4.0, SD 0.9; P<.001) and felt it was easy to make rational decisions (mean 4.1, SD 1.1) compared with those with poor knowledge (mean 3.8, SD 1; P<.001). However, intentions to reduce and prescribe antibiotics were not affected by attitudes, subjective norms, or perceived behavioral control, and perceived antibiotic prescribing behavior was not related to intentions to reduce or prescribe antibiotics. CONCLUSIONS: Antibiotic prescribing behavior was not under the volitional control of physicians. This calls for a systematic approach to change antibiotic prescribing practices in hospital.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».