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Record W3109207274 · doi:10.3138/jvme-2019-0096

Correlation between Student Self-Assessment and Proctor Evaluation in a Veterinary Surgical Laboratory

2020· article· en· W3109207274 on OpenAlexvenueno aff
Sarah Schock, Stephanie L. Shaver, Breanne Craigen, Erik H. Hofmeister

Bibliographic record

VenueJournal of Veterinary Medical Education · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumSelf-assessmentSignificant differenceMedicinePsychologyMedical educationPedagogyInternal medicine

Abstract

fetched live from OpenAlex

Self-assessment has been shown to facilitate learning, goal setting, and professional development. We sought to evaluate whether veterinary students in a surgical curriculum would have self-assessments that differed from proctor evaluations and whether high-performing students would differ from low-performing students in self-assessment characteristics. Student and proctor assessments were compared for 8 weeks of a spay/neuter surgical laboratory taking place in the second year of the curriculum. Eight students were classified as high-performing, and 10 students were classified as low-performing. A quantitative evaluation of the scores and a qualitative assessment of written comments were completed. Proctors assigned higher scores to high-performing students compared to low-performing students, but no difference was observed overall in self-assessment scores assigned by students. When only anesthesia students were evaluated, we found a difference in self-assessment scores for high- versus low-performers, but this was not true for surgery students. Differences between proctor and student assessment scores diminished over time for all students and for anesthesia students, but not for surgery students. High-performing student anesthetists self-assessed and received proctor assessments with higher scores in technical skills. Comments from high-performing students tended to be less reflective and more positive. Low-performing students were more defensive and more likely to use I-statements in their comments. Overall, quantitative analysis did not reveal a difference in self-assessment between high-performers and low-performers; however, specific differences existed in qualitative characteristics, surgery versus anesthesia students, and proctor assessments. The differences in self-assessment between high- and low-performing students suggest areas of further investigation for the use of reflection in education.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.072
GPT teacher head0.464
Teacher spread0.392 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2020
Admission routes1
Has abstractyes

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