Optimizing the Reliability of Communication Skills Assessment in Clinical Dentistry: A Generalizability Study
Bibliographic record
Abstract
Introduction Communication skills are universally recognised as a core competency. This study aims to develop and validate an CCG-adapted instrument for communication skills in clinical settings that can be used by trainees themselves (self-assessment). Methods This study was conducted at Damascus University Dental School, Syria in 2024 within authentic clinical settings. Based on Calgary Cambridge Guide, a 31-item assessment instrument was developed, translated and cross-culturally validated for the Syrian Arabic context. An assessment comprised six consultation sessions with six patients (three real and three standardized) was conducted. After each consultation, students and patients completed the assessment form. A balanced, fully crossed, three-facet design Generalizability (G) and decision (D) studies were used to assess reliability for student and patient assessments. Structural validity was evaluated using exploratory factor analysis (EFA) . Results Self-assessment demonstrated excellent reliability (G-coefficient = 0.93), reaching >0.90 with four cases. Patient assessments showed moderate reliability (G = 0.66), with Decision studies projecting improvements to 0.72 with 8 raters, 0.80 with 13 raters, and ~0.90 with ≥30 raters. Reliability estimates for real patients (G = 0.51) were comparable to standardized patients (G = 0.55), with differences narrowing as rater numbers increased. Exploratory factor analysis indicated a one-factor solution explaining 52.1% of the variance. Conclusion A validated self-assessment tool can foster communication skills development, while real patients provide reliability comparable to standardized patients, offering cost-effective, authentic, and participatory approaches. This first Arabic version of the tool addresses training gaps in communication skills in the Arab region.
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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 | 0.108 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".