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Record W1986328446 · doi:10.3138/jvme.0814-083r

Relationship between Anticipatory Socialization Experiences and First-Year Veterinary Students' Career Interests

2015· article· en· W1986328446 on OpenAlexvenueno aff
April A. Kedrowicz, Richard E. Fish, Sarah Hammond

Bibliographic record

VenueJournal of Veterinary Medical Education · 2015
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSocializationSpecialtyMedical educationVocational educationCareer pathPsychologyVeterinary medicinePrivate practiceCareer developmentMedicineFamily medicinePedagogyManagementSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this project was to explore first-year veterinary students' anticipatory socialization-life, education, and social experiences that assist in preparation for professional occupations-and determine what relationship exists between those experiences and career interests. Seventy-three first-year veterinary students enrolled in the Careers in Veterinary Medicine course completed the Veterinary Careers survey. Results show that students' anticipatory vocational socialization experiences are significantly related to their stated career interests. The career interests with the highest percentage of students expressing "a great deal of interest" included specialty private practice (37%), research and teaching in an academic setting (33%), and international veterinary medicine (31%). The career interests with the highest percentage of students expressing "no interest at all" included the military (50%), equine private practice (42%), and the pharmaceutical industry (41%). Less than half of the students (42%) stated that they reconsidered their career path after the first semester of veterinary school, but the majority (87%) developed a better understanding of how to pursue a nontraditional career path should they choose to do so.

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)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.160
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.696
GPT teacher head0.608
Teacher spread0.088 · 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.

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

Citations6
Published2015
Admission routes1
Has abstractyes

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