Rater cognition: review and integration of research findings
Bibliographic record
Abstract
BACKGROUND: Given the complexity of competency frameworks, associated skills and abilities, and contexts in which they are to be assessed in competency-based education (CBE), there is an increased reliance on rater judgements when considering trainee performance. This increased dependence on rater-based assessment has led to the emergence of rater cognition as a field of research in health professions education. The topic, however, is often conceptualised and ultimately investigated using many different perspectives and theoretical frameworks. Critically analysing how researchers think about, study and discuss rater cognition or the judgement processes in assessment frameworks may provide meaningful and efficient directions in how the field continues to explore the topic. METHODS: We conducted a critical and integrative review of the literature to explore common conceptualisations and unified terminology associated with rater cognition research. We identified 1045 articles on rater-based assessment in health professions education using Scorpus, Medline and ERIC and 78 articles were included in our review. RESULTS: We propose a three-phase framework of observation, processing and integration. We situate nine specific mechanisms and sub-mechanisms described across the literature within these phases: (i) generating automatic impressions about the person; (ii) formulating high-level inferences; (iii) focusing on different dimensions of competencies; (iv) categorising through well-developed schemata based on (a) personal concept of competence, (b) comparison with various exemplars and (c) task and context specificity; (v) weighting and synthesising information differently, (vi) producing narrative judgements; and (vii) translating narrative judgements into scales. CONCLUSION: Our review has allowed us to identify common underlying conceptualisations of observed rater mechanisms and subsequently propose a comprehensive, although complex, framework for the dynamic and contextual nature of the rating process. This framework could help bridge the gap between researchers adopting different perspectives when studying rater cognition and enable the interpretation of contradictory findings of raters' performance by determining which mechanism is enabled or disabled in any given context.
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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 | 0.022 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".