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Record W4384407743 · doi:10.1002/jdd.13334

e‐Prescribing in pediatric dentistry: Lessons learned from e‐learning module incorporation

2023· article· en· W4384407743 on OpenAlexaffabout
Michelle F. Siqueira, Keith Da Silva, Marie Rocchi, Walter L. Siqueira

Bibliographic record

VenueJournal of Dental Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of TorontoUniversity of Saskatchewan
Fundersnot available
KeywordsLibrary scienceMedicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.126
GPT teacher head0.482
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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