Dental Students’ Perceptions of an After‐Visit Summary in an Academic Clinical Setting: Mixed‐Method Approach
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate dental students' perceptions of an after-visit summary (AVS) at the University of Illinois Chicago, College of Dentistry, using a mixed-method approach. METHODS: An AVS module was created in the electronic health record (EHR, axiUm, Exan, Coquitlam, BC, Canada) for the most frequently performed dental procedures. The AVS was made available to all 3rd- and 4th-year dental students. Student utilization of the EHR-based AVS module was assessed for the classes of 2023, 2024, and 2025. Perceptions of the AVS were thereafter assessed by (a) an anonymous electronic survey disseminated to the class of 2024 (N = 118) at 6- and 16-months post-implementation and (b) a dental student focus group discussion (N = 9). Descriptive analyses were performed on the survey results. The focus group discussion was audio-recorded, transcribed, and qualitatively analyzed. RESULTS: The EHR-based AVS module was used by most students, with the highest compliance among the class of 2025 (85.6%). Of the 6-month survey respondents (N = 85), a majority (57.6%) utilized the AVS at least once. Most individuals utilizing the AVS reported improvements in communication of postoperative instructions and reduced time spent re-educating patients on treatment plans. Results were consistent with the focus group discussion. CONCLUSION: Dental students conveyed that their educational experiences were enhanced with the AVS, perceived it as a helpful and empowering tool for communicating with patients and caregivers, and intend to continue using the AVS in clinics to optimize the delivery of patient care.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".