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Record W2003997131 · doi:10.3138/jvme.36.3.284

A Descriptive Analysis of Personality and Gender at the Louisiana State University School of Veterinary Medicine

2009· article· en· W2003997131 on OpenAlexvenueno aff
S. Johnson, Marjorie S. Gill, Charles E. Grenıer, Joseph Taboada

Bibliographic record

VenueJournal of Veterinary Medical Education · 2009
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPersonalityPsychologyCohortNorm (philosophy)PopulationDescriptive statisticsDemographyVeterinary medicineClinical psychologyMedicineSocial psychologySociologyPathologyPolitical science

Abstract

fetched live from OpenAlex

The goals of this study were to explore the Myers-Briggs Type Indicator profile and gender differences of Louisiana State University veterinary students. A 12-year composite sample (N = 935) revealed that the personality profile was different from the published US population norm, but similar to the bimodal ESTJ-ISTJ profile found in Louisiana medical students. Significant gender differences were found among six of the 16 types. A 12-year trend analysis revealed a significant shift away from the prototypical ESTJ-ISTJ profile, culminating in a discernable heterogeneous profile for both males and females in the last four years. Composite scores for the 2004-2007 cohort (N = 331) revealed that the predominant types for women were ENFP, ESFJ, ESTJ, ISFJ, and ISTJ. For men, the predominant types were ESTJ, ESTP, INTP, and ISTJ. Post hoc tests confirmed significant gender differences for ESTP, INTP, ISTP, and ESFJ types. The evidence of significant gender differences and confirmation that personality profiles have begun to vary widely across the Myers-Briggs Type Indicator spectrum in the last four years have implications at the practical and theoretical levels. This could have profound effects on pedagogical considerations for faculty involved in veterinary medical 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 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)0.002
metaresearch head score (Gemma)0.002
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 score0.610
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.389
GPT teacher head0.516
Teacher spread0.127 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

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