e‐Prescribing in pediatric dentistry: Lessons learned from e‐learning module incorporation
Notice bibliographique
Résumé
Accurate medication prescribing and effective communication of this pharmacological information to patients is critical in dentistry.1, 2 To ensure that healthcare providers are prepared to prescribe medications accurately and safely, the Association of Faculties of Pharmacy of Canada (AFPC) created an e-Prescribing learning module specific for pharmacy, pharmacy technician, medical, nurse practitioner, and dental programs.3 The problem addressed in this study was the deficit in the knowledge and skills of dental students in prescribing appropriate medications for pediatric patients. Students had theoretical knowledge during the pharmacology course, but struggled with determining the appropriate medication, dosage, and regimen for pediatric patients in a clinical scenario as their prior pharmacology courses mainly focused on adult prescriptions. To address this problem, the AFPC e-Prescribing learning module3 was incorporated into the DENT424—Pediatric Dentistry Diagnosis and Treatment planning course. Students were required to register and complete the module on their own time, and then participate in live discussion of pediatric case studies with their peers and the DENT424 course instructor to practice and reinforce key concepts. Even though the module was multidisciplinary, it was relevant and effective in teaching students about the broader healthcare system, including the importance of data security with regard to dental records and the process involved in writing prescriptions, refills, and renewals. Initial verbal feedback from the students was positive. We perceived that the students demonstrated a better understanding of the overall process and logistics involved in prescribing medication, including a better understanding of clinician and patient perspectives. All 36 students (100%) participated in an anonymous survey conducted a week after completing the module, and 89% of students strongly agreed or agreed that the topics covered in the module were relevant, 91% of students strongly agreed or agreed that the module increased their knowledge about e-Prescribing, while 80% strongly agreed or agreed that the module helped them understand the role of other healthcare professionals in prescribing and the medication use system (Table 1). Feedback in Table 2 shows that 33% of students who provided written comments on the survey reported a lack of dentistry-specific examples, and another 33% stated the module was too long. Incorporating the e-Prescribing learning module and case discussions in pediatric dentistry was a positive supplemental learning opportunity for doctor of dental medicine (DMD) Year 3 students. The module was effective in teaching students about the broader healthcare system and all the processes involved in prescribing medication. However, there were areas of weakness that persisted, such as students' difficulty in choosing an appropriate medication, determining the correct dosage and regimen, and physically writing out a prescription. In the absence of a specific e-prescribing module for pediatric patients, future sessions in the course can include additional case discussions and practice time to increase competency, combined with additional practice in pediatric dentistry prescription skills as students progress through their final year. The overall positive feedback from the anonymous survey demonstrated that students enjoyed the experience of learning using e-Prescribing as an additional learning tool.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».