Peer assessment in medical communication skills training in programmatic assessment: A qualitative study examining faculty and student perceptions
Bibliographic record
Abstract
INTRODUCTION: Current literature recommends assessment of communication skills in medical education combining different settings and multiple observers. There is still a gap in understanding about whether and how peers assessment facilitates learning in communication skills training. METHODS: We designed a qualitative study using focus group interviews and thematic analysis, in a medical course in the Netherlands. We aimed to explore medical students' and teachers' experiences, perceptions, and perspectives about challenges and facilitating factors in PACST (Peer assessment in medical communication skills training). RESULTS: Most of the participants reported that peer feedback was a valuable experience when learning communication skills. The major challenges for the quality and credibility of PACST reported by the participants are the question whether peer feedback is critical enough for learning and the difficulty of actually engaging students in the assessment process. CONCLUSION: Teachers reviewing students' peer assessments may improve the quality and their credibility and the reviewed assessments can best be used for learning purposes. We suggest to pay sufficient attention to teachers' roles in PACST, ensuring a safe and trustworthy environment and additionally helping students to internalize the value of being vulnerable during the evaluation process.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".