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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".