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Record W2577924138 · doi:10.1111/medu.13158

Cracking the code: residents’ interpretations of written assessment comments

2017· article· en· W2577924138 on OpenAlexaff
Shiphra Ginsburg, Cees van der Vleuten, Kevin W. Eva, Lorelei Lingard

Bibliographic record

VenueMedical Education · 2017
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWestern UniversityUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)Meaning (existential)PsychologyGrounded theoryReading (process)NarrativeMedical educationConstruct (python library)Face (sociological concept)Set (abstract data type)Qualitative researchSocial psychologyMedicineLinguisticsSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

CONTEXT: Interest is growing in the use of qualitative data for assessment. Written comments on residents' in-training evaluation reports (ITERs) can be reliably rank-ordered by faculty attendings, who are adept at interpreting these narratives. However, if residents do not interpret assessment comments in the same way, a valuable educational opportunity may be lost. OBJECTIVES: Our purpose was to explore residents' interpretations of written assessment comments using mixed methods. METHODS: Twelve internal medicine (IM) postgraduate year 2 (PGY2) residents were asked to rank-order a set of anonymised PGY1 residents (n = 48) from a previous year in IM based solely on their ITER comments. Each PGY1 was ranked by four PGY2s; generalisability theory was used to assess inter-rater reliability. The PGY2s were then interviewed separately about their rank-ordering process, how they made sense of the comments and how they viewed ITERs in general. Interviews were analysed using constructivist grounded theory. RESULTS: Across four PGY2 residents, the G coefficient was 0.84; for a single resident it was 0.56. Resident rankings correlated extremely well with faculty member rankings (r = 0.90). Residents were equally adept at reading between the lines to construct meaning from the comments and used language cues in ways similarly reported in faculty attendings. Participants discussed the difficulties of interpreting vague language and provided perspectives on why they thought it occurs (time, discomfort, memorability and the permanency of written records). They emphasised the importance of face-to-face discussions, the relative value of comments over scores, staff-dependent variability of assessment and the perceived purpose and value of ITERs. They saw particular value in opportunities to review an aggregated set of comments. CONCLUSIONS: Residents understood the 'hidden code' in assessment language and their ability to rank-order residents based on comments matched that of faculty. Residents seemed to accept staff-dependent variability as a reality. These findings add to the growing evidence that supports the use of narrative comments and subjectivity in 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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.023
GPT teacher head0.441
Teacher spread0.418 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations67
Published2017
Admission routes1
Has abstractyes

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