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Record W2767325736 · doi:10.1111/medu.13476

Emotions and assessment: considerations for rater‐based judgements of entrustment

2017· review· en· W2767325736 on OpenAlexaff
Carlos Gomez‐Garibello, Meredith Young

Bibliographic record

VenueMedical Education · 2017
Typereview
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsMcGill University
Fundersnot available
KeywordsFormative assessmentSummative assessmentScrutinyPsychologyContext (archaeology)CognitionNarrativeEducational assessmentApplied psychologyMedical educationPedagogyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.169
GPT teacher head0.536
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations23
Published2017
Admission routes1
Has abstractyes

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