Alignment Between Classroom Education and Clinical Practice of Root Canal Treatment Among Dental Practitioners in China: Cross-Sectional Study
Bibliographic record
Abstract
Background: This cross-sectional study assessed the perceived alignment between preclinical education and clinical practice in root canal treatment (RCT) among dental practitioners in China, aiming to identify systemic gaps in dental curricula and their clinical implications. Objective: Dental professionals in Eastern Coastal China. This study distributed questionnaires through hospital dental specialties and medical forums, covering the Southeastern Region of China. Methods: A validated, web-based survey was distributed to 90 dental professionals in Eastern Coastal China, focusing on 9 key stages of RCT, preoperative preparation, intraoperative procedures, postoperative care, and clinician-patient communication. Responses were measured using a 7-point Likert scale to evaluate perceived discrepancies between education and clinical practice. Results: A total of 83 valid questionnaires were recovered, which revealed significant disparities between academic training and clinical demands. The survey showed that the specialized practitioners identified pronounced mismatches in RCT operative techniques and doctor-patient communication (P<.05). Participants aged ≤29 years demonstrated heightened awareness of discrepancies in disinfection protocols and temporary filling procedures (P<.05). Shanghai-trained practitioners reported fewer educational-clinical gaps across multiple procedural stages (P<.05). Notably, 82% of respondents rated comprehensive RCT implementation as more challenging than individual procedural components. Curriculum deficiencies were identified in treatment indication diagnostics (56.6% agreement) and communication training (43.4% agreement). Emerging technologies like virtual reality and augmented reality (VR and AR) showed minimal educational penetration (3.7% exposure rate). In the free-response section, qualitative feedback highlighted equipment accessibility issues (eg, thermal gutta-percha tools) and instructor-dependent learning outcomes. Conclusions: Structural discrepancies exist in Chinese preclinical RCT education, influenced by factors such as experience level, age, and region. These findings underscore the need for curriculum reforms, emphasizing competency-based training, enhanced simulation technologies, and standardized clinical protocols, particularly in areas like periodontal pathology and communication skills.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".