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Record W2248658410 · doi:10.3138/jvme.0315-039r1

Veterinary Students' Recollection Methods for Surgical Procedures: A Qualitative Study

2015· article· en· W2248658410 on OpenAlexvenueno aff
Rikke Langebæk, Lene Tanggaard, Mette Berendt

Bibliographic record

VenueJournal of Veterinary Medical Education · 2015
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationRecallMedicineQualitative researchSurgical proceduresVeterinary medicinePsychologySurgerySociology

Abstract

fetched live from OpenAlex

When veterinary students face their first live animal surgeries, their level of anxiety is generally high and this can affect their ability to recall the procedure they are about to undertake. Multimodal teaching methods have previously been shown to enhance learning and facilitate recall; however, student preferences for recollection methods when translating theory into practice have not been documented. The aim of this study was to investigate veterinary students' experience with recollection of a surgical procedure they were about to perform after using multiple methods for preparation. From a group of 171 veterinary students enrolled in a basic surgery course, 26 students were randomly selected to participate in semi-structured interviews. Results showed that 58% of the students used a visual, dynamic method of recollection, mentally visualizing the video they had watched as part of their multimodal preparation. A mental recipe was used by 15%, whereas 12% mentally visualized their own notes. The study provides new information regarding veterinary students' methods of recollection of surgical procedures and indicates that in Danish veterinary students, a visual dynamic method is the most commonly used. This is relevant information in the current educational situation, which uses an array of educational tools, and it stresses the importance of supporting the traditional surgical teaching methods with high-quality instructional videos.

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)0.014
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.677
GPT teacher head0.721
Teacher spread0.043 · 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 designQualitative
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

Citations11
Published2015
Admission routes1
Has abstractyes

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