Should attendance for preclinical simulation and clinical education be mandatory?
Bibliographic record
Abstract
Mandatory attendance, particularly in didactic settings, is a highly debated topic in higher education, including dental education. Within dental education, a large portion of education occurs in preclinical laboratories and clinical environments. There is little to no research on attendance in these settings in dental schools. This point/counterpoint paper examines the pros and cons of mandatory attendance in these highly specialized educational settings. With the backdrop of the COVID-19 pandemic that began in March 2020 and continues to impact dental education at the time of publication, this topic has become even more relevant. Viewpoint 1 claims that attendance should be mandatory because a greater exposure to preclinical and clinical environments helps foster better clinical hand skills, critical thinking, decision-making, problem-solving skills, and an overall sense of professional identity. It goes on further to suggest that there may be a link between attendance and performance in exams and that attendance is part of the dental school's responsibility. Viewpoint 2 argues that the rationale for attendance is complex, and that creating learning environments that are psychologically safe will incentivize students to attend, even without mandatory attendance policies. Furthermore, it explains that technological advances have allowed dental schools to think creatively about asynchronous learning, which by its very nature does not require attendance at a given time. The authors of both viewpoints conclude that the preclinical and clinical education and experience are critical dental education and that dental school leaders should focus on improving the quality of these experiences.
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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 | 0.020 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".