A cross-sectional study on dentists’ learning preferences for learning about light-curing units and resin-based composites
Bibliographic record
Abstract
• Most participants lacked knowledge about their LCU type. • Wavelength is rarely considered when choosing the LCU. • Hands-on workshops are preferred for education. • YouTube was the preferred social media platform for learning. • Graduate/postgraduates had better knowledge about LCUs than those with a only a bachelor’s degree. Despite researchers emphasizing the importance of understanding the type, how they should be used, and their effect on restoration longevity, many dentists lack critical knowledge about their light curing units (LCUs). To identify the parameters dentists use when choosing an LCU or resin-based composite (RBC) and to determine the most effective educational method for dentists to learn about LCUs. A cross-sectional study used a validated electronic questionnaire targeting dentists, interns, and students in private and government sectors. Demographic data, current LCU practices, parameters for selecting LCUs and RBCs, and the preferred educational methods for learning about LCUs were collected. The three main parameters used to determine participants’ knowledge of LCU selection were brand reputation, irradiance, and LCU wavelength compatibility with the RBC photoinitiator spectrum. Results were analyzed using chi-square test, Fisher’s exact test, and independent sample t -test. A total of 420 participants completed the survey. Only 11 % considered the wavelength as the 1st parameter when selecting an LCU. Participants with graduate degrees were significantly more knowledgeable about the LCU selection parameters than those with bachelor’s degrees (p = 0.009). Dentists who frequently used LCUs knew significantly more about the RBC selection parameters (p = 0.037). Hands-on workshops, in-person lectures, and online lectures were preferred over social media. YouTube is one of the most popular social media platforms. All participants preferred YouTube as the top social media educational resource, with specialists and consultants showing a significantly stronger preference than general dentists, interns, and students (p = 0.036). Few participants thought that the need for the wavelengths emitted by the LCU to be compatible with the absorption spectrum of the photoinitiator used in the RBC was a key parameter when selecting an LCU or RBC. Respondents favored hands-on workshops and in-person lectures over social media to learn about LCUs. Educators should prioritize these methods to enhance understanding the choice and use of LCUs and RBCs within the dental community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".