Peer assessment approach to promote clinical communication skills in a blended learning course of early clinical exposure
Bibliographic record
Abstract
Background: Blended learning offers opportunities for the complexity of learning in clinical education. Student peer assessment is widely used as a form of formative assessment in early clinical exposure programs, especially clinical communication skills training. This study aimed to describe clinical communication skills competencies of second-year students and to identify the relationships between peer and faculty assessment of communication skills in a blended learning program format. Methods: A total of 474 second-year general medical students and dental students participated in the study. Peer and lecturer assessment forms with a 5-point Likert scale according to the Calgary-Cambridge guide format were used to evaluate students’ performance of basic communication skills, relationship building, and history taking. Pearson’s correlation coefficients and paired t-test were applied. A p-value < 0.05 was considered statistically significant. Results: Most of students were rated at distinction level (score at 7-8.4) in communication skills. Mean of the overall score by peer and faculty assessment were 7.46 ± 1.03 and 7.17 ± 0.68, respectively. Peers rarely provided negative ratings on subcategories of communication skills. Skills of understanding the patient’s perspectives and gathering information were the most reported skills needed to improve among students. Significant positive correlations were found between peer and faculty evaluations for building relationship, establishing initial rapport, and gathering information domains (p < 0.01). Students tended to grade their colleagues higher for building relationship (3.88 ± 0.62) and establishing initial rapport domains (3.72 ± 0.61) than other domains, meanwhile, teachers tended to grade building relationship (3.80 ± 0.55) and gathering information domains (3.64 ± 0.38) higher than other domains. Conclusion: The findings suggest that student peer evaluation can be valuable for clinical education. As part of a formative assessment, it can be also used for faculty to evaluate students’ clinical communication skills performance in innovative medical education programs. Key words: peer assessment, clinical communication skills, practice of medicine, early clinical practice, blended learning.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".