Assessing Dental Students’ Emotions While Treating Uncooperative Children: A Pilot Study
Bibliographic record
Abstract
Introduction: Dental students often face emotional challenges when managing uncooperative pediatric patients, which may lead to reduced interest in treating children post-graduation, limiting access to pediatric dental care. Objective: This study aimed to examine dental students' emotional experiences while treating uncooperative children and their perceptions of the training provided on behavioral management techniques. Materials and Methods: This cross-sectional survey-based pilot study was conducted at an urban, university-affiliated dental clinic during the 2017-2018 academic year. The target population consisted of 41 third-year dental students in their first year of clinical rotations, from which 29 students completed the survey. Informed consent was obtained, emphasizing voluntary participation or withdrawal. Students were invited to complete an 18-question Likert-scale survey. Access was restricted to their unique university emails, ensuring participation integrity. To avoid academic pressure, a research assistant administered the survey. The survey included questions adapted from established tools such as the State-Trait Anxiety Inventory (STAI) and validated by two pediatric dentistry experts. Responses were analyzed using descriptive statistics and path analysis to explore relationships between stress, training adequacy, and behavioral management perceptions. Results: A majority (75.9%) of students reported stress while treating uncooperative children, with 58.6% experiencing anxiety and 69.0% frustration. Additionally, 13.8% expressed concerns about harming the child. While 51.7% were satisfied with their treatment outcomes, 65.5% indicated needing further behavioral management training. Conclusion: This study highlighted significant stress and anxiety among managing uncooperative pediatric patients. Enhanced training in behavioral management and increased clinical exposure are essential to prepare students for practice confidently.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".