Rapid redesign and implementation of new preclinic and clinic scheduled model during COVID‐19
Bibliographic record
Abstract
The College of Dentistry's DMD program must meet national standards for accreditation and its academic programming is structured to meet these standards. The COVID-19 pandemic required the college to rethink its academic schedule and devise an approach that simultaneously maintained academic programming requirements and minimized COVID-19-related risks from in-person activities for students and instructors. Before the COVID-19 pandemic, the college traditionally provided two daily clinic and/or preclinic sessions in the morning (9–11:30 AM) and in the afternoon (1:30–4:30 PM), and didactic lectures were typically taught in-person for about 1 h before, in between, and/or after those sessions. Students also had access to 1 h lunch break. This schedule required students to spend a large portion of their day in-person at the college. In devising the new schedule, various constraints needed to be considered, including the specific technical requirements students needed to accomplish during preclinical/clinical activities, restricted access to college spaces for students before/after hands-on activities due to COVID-19, as well as logistical constraints to avoid students commuting to campus multiple times a day (eliminating unnecessary stress from traffic, parking, or bus delay). The college's solution was to shift to remote lectures and modify the clinic and preclinic session times into a compressed single session held back-to-back (8:30–2:30 PM) with only a small mid-session break. Students were asked to leave campus as soon as the compressed session came to an end. Synchronous remote lectures were all rescheduled to occur later in the afternoon and start times took into consideration student travel time from the college to their homes. Pre-recorded lectures were also available in some disciplines to be watched at any desired time. A total of 67.5% of students reported being either satisfied or very satisfied with the new preclinic/clinic scheduling model (Figure 1). Also, 66.3% of students reported this model provided more time in the evening to study and for personal activities (Figure 2). Instructors did not report a decline in the quality of dental work provided by the students, and there was not an increase in repeated treatment due to poor quality. However, some challenges were encountered, especially early in the term, with regard to time management. Students were required to complete treatment, be evaluated, and follow a lengthy cleaning and disinfection protocol in a shorter time compared to pre-pandemic, where clinic and preclinic sessions were longer and there was less pressure to adhere to strict clinic end times. Over time, slight modifications to our clinical protocols were made without any prejudice to patient treatment to allow more time-efficient clinical sessions; for example, when students confirm patient appointments by phone, they now also complete health screening with the patient and update the patient's chart remotely, allowing extra time at the in-person appointment for actual treatment.1, 2 During that phone call, students also ask COVID-19 screening questions and advise patients about current College of Dentistry COVID-19 protocols.3 Given the success, this model might endure even after the COVID-19 pandemic concludes, with a whole new generation of dentistry students trained in this new time-efficient clinical and preclinical model. The silver lining is that these compressed clinical/preclinical sessions have not impacted the quality of patient care, and students are cultivating efficient work habits that will be valuable in the private practice setting following graduation.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".