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Record W2001198186 · doi:10.3138/jvme.37.2.154

Factors Affecting Track Selection by Veterinary Professional Students Admitted to the School of Veterinary Medicine at the University of California, Davis

2010· article· en· W2001198186 on OpenAlexvenueno aff
Munashe Chigerwe, Karen A. Boudreaux, Jan E. Ilkiw

Bibliographic record

VenueJournal of Veterinary Medical Education · 2010
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGraduation (instrument)Track (disk drive)CurriculumVeterinary medicineMedical educationFast trackOddsMedicineSelection (genetic algorithm)PsychologyInternal medicineLogistic regressionPedagogyEngineeringComputer scienceSurgery

Abstract

fetched live from OpenAlex

Factors affecting track selection before admission to the School of Veterinary Medicine at the University of California, Davis, and factors affecting change of tracks after the first two years of the curriculum were investigated by means of a survey of the 118 students of the graduating class of 2009. The student's background experience before admission to the School of Veterinary Medicine and other personal reasons were significant factors affecting small-animal and mixed-animal track choices. The student's background experience before admission to the School of Veterinary Medicine was the only significant factor for choosing the zoological track. The most significant factor for students to change their track from the mixed or zoological track to the small-animal track was background experience before admission to the School of Veterinary Medicine. Anticipated increased employment opportunities after graduation was the most significant factor for students to change their track from the mixed- or small-animal track to the zoological track. Other personal reasons was the significant variable for students to change their track from small-animal or zoological to mixed-animal track. Thus, to increase the number of students interested in tracks with lower enrollment, exposure of potential applicants to experience relevant to that track before admission and Admissions Committee selection criteria are likely to increase the odds of students' choosing that track.

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)0.003
metaresearch head score (Gemma)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.214
GPT teacher head0.511
Teacher spread0.297 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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