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Record W2984133515 · doi:10.3138/jvme.0718-081r

A Model Course to Enhance Veterinary Student Exposure to Research

2019· article· en· W2984133515 on OpenAlexvenueno aff
Elliott S. Chiu, Elizabeth W. Goldsmith, Caroline S. Moon, Sue VandeWoude

Bibliographic record

VenueJournal of Veterinary Medical Education · 2019
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMedical educationVeterinary educationClass (philosophy)Veterinary medicineCourse (navigation)Process (computing)PerceptionPsychologyMedicinePedagogyEngineeringComputer science

Abstract

fetched live from OpenAlex

Despite many career opportunities available to veterinarians in research related fields and requirements for training in research methodologies by the American Veterinary Medical Association Council on Education (AVMA COE), formal approaches to development of veterinary curriculum related to research topics have not been widely reported. Colorado State University (CSU) offers a one-credit course that introduces first-year veterinary students to skills and career opportunities in research. Here we provide information about the course structure and content, and report outcomes of survey data that assesses the impact of the course on student appreciation and understanding of the research process. We found that most United States (US) veterinary colleges do not offer a didactic course on the research process. Student opinions of veterinary researchers were generally high, though a proportion of students (30%-40%) would have preferred a practice management class to a course on research principles. Nearly 25% of students reported that they were significantly influenced to consider research careers after taking the course. We document that this one-credit seminar course improved veterinary student perceptions of their understanding of the research process and resulted in self-reported influence of career choice.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.548
GPT teacher head0.669
Teacher spread0.121 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

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