Effect of Training Method on Dental Students’ Light‐Curing Performance
Bibliographic record
Abstract
The aims of this study were to evaluate 1) the effect of different light-curing training methods on first-year dental students' light-curing performance immediately after and one year after training and 2) the effect of dental student year (first vs. third year) and different light-curing training methods on students' light-curing efficacy. The study was organized into two parts, conducted in 2013 and 2014. In the first part, all students (N=66) in the first-year restorative course at a Canadian dental school were invited to participate in a light-curing activity immediately after training; after 12 months, those students (now in their second year) were asked to again perform the light-curing activity. In the second part, all the subsequent first-year students (N=75) and third-year students (N=95) were invited to perform the same light-curing activity after training. Each class was divided into three groups: the Control group received a lecture; the Tips group received the same lecture plus tips on "7 Steps to Ensure Better Light Curing"; and the Simulator group received the lecture, tips, and hands-on training using the patient simulator. In the first part of the study, all 66 students participated in the first year; 39 of those participated a year later. In the second part, all students in both years participated. For the first part, time had a significant effect on light-curing performance, with significantly higher results in the second year. When the first-year students' results were isolated, training method had a significant effect on mean and maximum irradiance values. The second part found a significant effect of training on mean (p=0.003) and maximum (p=0.021) irradiance. The interaction DDS*training was significant (p=0.037) when the after-training values were analyzed. These results point to the need for further research to determine the most effective light-curing training methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".