Evaluating Communication Skills in Dental Students: Correlation Between Self-Assessment, Patient Perspectives, and Evaluator Rating
Bibliographic record
Abstract
Background: Effective communication is a vital skill in the dental profession and plays an important role in patient interactions. This study aimed to assess the self-evaluation of clinical communication skills by dental students at Mashhad University of Medical Sciences and to compare it with the perspectives of patients and an evaluator.Methods and Materials: This descriptive cross-sectional study was conducted at Mashhad Dental School and included final-year undergraduate dental students. To evaluate communication skills, the Calgary-Cambridge Observation Guide checklist was used, consisting of 71 items across six domains. Each item was rated by the student, the patient, and the evaluator using a three-point Likert scale (Yes, To some extent, No). Total scores ranged from 71 to 213, and communication skills were categorized into three levels: poor (71–118.33), moderate (118.34–165.66), and good (>165.67). Statistical analyses were conducted using the Shapiro-Wilk test, one-way ANOVA, and Spearman correlation test. (α = 0.05)Results: The mean scores of students (157.8 ± 19.3), patients (147.3 ± 29.2), and the evaluator (122.7 ± 20.3) showed statistically significant differences. A significant correlation was found between the students’ self-assessments and patients’ evaluations (P < 0.001, r = 0.59) as well as between the students and the evaluator (P = 0.006, r = 0.41). The correlation between student and evaluator scores was observed in the domains of interpersonal skills at the beginning of the session (r = 0.57), information gathering (r = 0.40), and explanation and planning (r = 0.46).Conclusion: Students generally rated their communication skills at a moderate level. Patients had a more favorable view compared to the evaluator, and students rated themselves higher than both groups. These results highlight the need to senhance communication skills training in the undergraduate dental curriculum.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".