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Record W2020765458 · doi:10.3138/jvme.0413-063r2

Validation of a Psychometric Instrument to Assess Motivation in Veterinary Bachelor Students

2014· article· en· W2020765458 on OpenAlexvenueno aff
Jean‐Michel Vandeweerd, Alex Dugdale, Marc Romainville

Bibliographic record

VenueJournal of Veterinary Medical Education · 2014
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaScale (ratio)PsychologyMedical educationBachelorConstruct validityReliability (semiconductor)CurriculumPsychological interventionAffect (linguistics)Likert scalePsychometricsVeterinary medicineApplied psychologyMedicineClinical psychologyPedagogy

Abstract

fetched live from OpenAlex

There are indications that motivation correlates with better performance for those studying veterinary medicine. To assess objectively whether motivation profiles influence both veterinary students' attitudes towards educational interventions and their academic success and whether changes in curriculum can affect students' motivation, there is need for an instrument that can provide a valid measurement of the strength of motivation for the study of veterinary medicine. Our objectives were to design and validate a questionnaire that can be used as a psychometric scale to capture the motivation profiles of veterinary students. Question items were obtained from semi-structured interviews with students and from a review of the relevant literature. Each item was scored on a 5-point scale. The preliminary instrument was trialed on a cohort of 450 students. Responses were subjected to reliability and principal component analysis. A 14-item scale was designed, within which two factors explained 53.4% of the variance among the items. The scale had good face, content, and construct validities as well as a good internal consistency (Cronbach's alpha=.88).

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)0.004
metaresearch head score (Gemma)0.008
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 score0.317
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.534
GPT teacher head0.585
Teacher spread0.051 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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