Enhancing Knowledge, Skills, and Confidence of Oral Health Professionals Through Head Simulator Training: A Perceived Benefit
Bibliographic record
Abstract
Researchers have revealed the advantages of experiential learning for students and professionals at all levels of the health care delivery system. The purpose of this study is to determine the effectiveness of the use of head simulators in dental school in acquiring proficient periodontal knowledge, dental skills, and confidence by practicing oral health professionals. Of the 117 purposive sampled participants surveyed using a 5-point Likert scale questionnaire, 60 respondents used head simulators during their dental school education. On the effect of the head simulator enhancing knowledge. The findings regarding the effect of the head simulator in dental school revealed varying perspectives. A significant majority of participants agreed the head simulator had a beneficial effect on their skills. Among these findings, a third of participants strongly agreed the use of the head simulator notably enhanced their skills. When considering the influence on knowledge, the responses were more evenly distributed. Almost 40% of participants agreed the head simulator positively affected their knowledge, while almost 20% of participants generally disagreed. Examining the effect on confidence, findings also depicted varying viewpoints among the participants with 42% acknowledging the head simulator had a positive effect on their confidence. The findings suggest that head simulators positively affect dental education, particularly in enhancing knowledge, skills, and confidence.
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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 | 0.003 | 0.013 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".