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Record W3024647963 · doi:10.3138/jvme.2018-0010

Full-Time Employment or Post-Graduate Education: A Means–Ends Investigation of Fourth-Year Veterinary Students

2020· article· en· W3024647963 on OpenAlexvenueno aff
Kathryn L. Mueller, Erik H. Hofmeister, Shane D. Lyon, Andrew D. Woolcock

Bibliographic record

VenueJournal of Veterinary Medical Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomyFraming (construction)Competence (human resources)Veterinary educationMedical educationGraduation (instrument)PsychologyPost graduatePedagogyVeterinary medicineCurriculumMedicinePolitical scienceSocial psychologyEngineering

Abstract

fetched live from OpenAlex

The purpose of this article is to identify the motivations for fourth-year veterinary students to pursue either full-time employment or post-graduate education. Twenty-one fourth-year veterinary students were interviewed using a means-ends investigation style. Interviews were analyzed using a qualitative method in the context of the self-determination theory pillars of intrinsic motivation (autonomy, competence, and relatedness). Students interested in full-time employment had more statements that were categorized as demonstrating an interest in autonomy. Students who were interested in post-graduate education had more statements that were categorized as demonstrating an interest in developing competence. Both groups of students indicated that relatedness was important. Understanding the beliefs and motivations that influence students' post-graduation career decisions is important in framing their decision-making process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
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 head0.465
GPT teacher head0.540
Teacher spread0.075 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2020
Admission routes1
Has abstractyes

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