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Record W2118879574 · doi:10.3138/jvme.36.3.305

Veterinary Student Attitudes toward Curriculum Integration at James Cook University

2009· article· en· W2118879574 on OpenAlexvenueno aff
John Cavalieri

Bibliographic record

VenueJournal of Veterinary Medical Education · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMedical educationIntegrated curriculumVeterinary medicinePsychologyMathematics educationMedicinePedagogy

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the attitudes of veterinary science students to activities designed to promote curriculum integration. Students (N = 33) in their second year of a five-year veterinary degree were surveyed in regard to their attitudes to activities that aimed to promote integration. Imaging, veterinary practice practicals, and a field trip to a cattle property were classified as the three most valuable learning activities that were designed to promote integration. Veterinary practice practicals, case studies, and palpable anatomy were regarded by students as helping them to learn information presented in other teaching sessions. They also appeared to enhance student motivation, and students indicated that the activities assisted them with their preparation for and performance at examinations. Attitudes to whether the learning exercises helped improve a range of skills and specific knowledge varied, with 39-88% of students agreeing that specific skills and knowledge were enhanced to a large or very large extent by the learning activities. The results indicate that learning activities designed to promote curriculum integration helped improve motivation, reinforced learning, created links between foundational knowledge and its application, and assisted with the development of skills that are related to what students will do in their future careers.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.166
GPT teacher head0.501
Teacher spread0.335 · 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 designNot applicable
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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