Assessors’ interpretations of narrative data on communication skills in a summative OSCE
Bibliographic record
Abstract
OBJECTIVES: Increasingly, narrative assessment data are used to substantiate and enhance the robustness of assessor judgements. However, the interpretation of written assessment comments is inherently complex and relies on human (expert) judgements. The purpose of this study was to explore how expert assessors process and construe or bring meaning to narrative data when interpreting narrative assessment comments written by others in the setting of standardised performance assessment. METHODS: Narrative assessment comments on student communication skills and communication scores across six objective structured clinical examination stations were obtained for 24 final-year pharmacy students. Aggregated narrative data across all stations were sampled for nine students (three good, three average and three poor performers, based on communication scores). A total of 10 expert assessors reviewed the aggregated set of narrative comments for each student. Cognitive (information) processing was captured through think-aloud procedures and verbal protocol analysis. RESULTS: Expert assessors primarily made use of two strategies to interpret the narratives, namely comparing and contrasting, and forming mental images of student performance. Assessors appeared to use three different perspectives when interpreting narrative comments, including those of: (i) the student (placing him- or herself in the shoes of the student); (ii) the examiner (adopting the role of examiner and reinterpreting comments according to his or her own standards or beliefs), and (iii) the professional (acting as the profession's gatekeeper by considering the assessment to be a representation of real-life practice). CONCLUSIONS: The present findings add to current understandings of assessors' interpretations of narrative performance data by identifying the strategies and different perspectives used by expert assessors to frame and bring meaning to written comments. Assessors' perspectives affect assessors' interpretations of assessment comments and are likely to be influenced by their beliefs, interpretations of the assessment setting and personal performance theories. These results call for the use of multiple assessors to account for variations in assessor perspectives in the interpretation of narrative assessment data.
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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 | 0.142 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".