Development and Validation of a Teaching Module for Prescription Writing for Dental Students: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVES: Dentists commonly encounter patients with complex medical comorbidities that require an advanced level of competence in the art of prescription writing. However, the current structure of dental education often places limited emphasis on this critical skill. This study aimed to develop and validate an innovative teaching module designed to enhance prescription-writing skills for dental students, with a specific focus on patients with medical comorbid conditions. METHODS: This study was completed in two phases. In phase 1, an interprofessional education (IPE) designed comprehensive teaching module was created. The topics included in this teaching module were medical comorbidities, drug interactions, and best prescription practices. The developed teaching module's face and content were validated, and the item- content validity index (I-CVI) was computed. In phase 2, the teaching module was tested among 48 dental students as part of a randomized controlled trial. RESULTS: A pool of eight items addressing different aspects related to prescription writing were validated in dental students. All the eight items reached an I-CVI for relevance and structure of ≥0.8. In phase 2, the intervention group, exposed to the teaching module on skill development of prescription writing, showed a statistically significant increase in their prescription-writing skill than the control group. CONCLUSION: The introduction of a teaching module aimed at developing prescriptions for medical co-morbidities could substantially improve the prescription writing abilities of dental students.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".