Emotions and assessment: considerations for rater‐based judgements of entrustment
Bibliographic record
Abstract
CONTEXT: Assessment is subject to increasing scrutiny as medical education transitions towards a competency-based medical education (CBME) model. Traditional perspectives on the roles of assessment emphasise high-stakes, summative assessment, whereas CBME argues for formative assessment. Revisiting conceptualisations about the roles and formats of assessment in medical education provides opportunities to examine understandings and expectations of the assessment of learners. The act of the rater generating scores might be considered as an exclusively cognitive exercise; however, current literature has drawn attention to the notion of raters as measurement instruments, thereby attributing additional factors to their decision-making processes, such as social considerations and intuition. However, the literature has not comprehensively examined the influence of raters' emotions during assessment. In this narrative review, we explore the influence of raters' emotions in the assessment of learners. METHODS: We summarise existing literature that describes the role of emotions in assessment broadly, and rater-based assessment specifically, across a variety of fields. The literature related to emotions and assessment is examined from different perspectives, including those of educational context, decision making and rater cognition. We use the concept of entrustable professional activities (EPAs) to contextualise a discussion of the ways in which raters' emotions may have meaningful impacts on the decisions they make in clinical settings. This review summarises findings from different perspectives and identifies areas for consideration for the role of emotion in rater-based assessment, and areas for future research. CONCLUSIONS: We identify and discuss three different interpretations of the influence of raters' emotions during assessments: (i) emotions lead to biased decision making; (ii) emotions contribute random noise to assessment, and (iii) emotions constitute legitimate sources of information that contribute to assessment decisions. We discuss these three interpretations in terms of areas for future research and implications for assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".