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Record W2034633568 · doi:10.3138/jvme.35.1.074

Part II: Directions and Objectives of Curriculum Structure at Veterinary Medical and Other Health Professions Schools

2008· review· en· W2034633568 on OpenAlexvenueno aff
Grant H. Turnwald, D. Phillip Sponenberg, J. Blair Meldrum

Bibliographic record

VenueJournal of Veterinary Medical Education · 2008
Typereview
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumHealth professionsMedical educationCurriculum developmentManaging changeMedicineEngineering ethicsPolitical scienceSociologyPublic relationsHealth carePedagogyEngineering

Abstract

fetched live from OpenAlex

This article surveys reports on various models and methods of curriculum structure and directions of health professions schools in North America over the past 20 years, with particular emphasis on veterinary and allopathic medical schools. The importance of administrative and faculty leadership, a clear sense of vision, recognition that curricula must change to meet societal needs, and continual, extensive communication and collaboration are discussed as important keys to successfully navigating curriculum reform. The advantages of central versus departmental management of the curriculum are noted with respect to implementing curricular change. Investment in faculty development is essential to ensure sustained cultural and curricular change. As instructional methodology changes, new and better methods of assessing student performance must be developed, with timely and appropriate feedback. Barriers to curriculum change are inevitable; effective strategies must be designed and implemented to navigate these barriers. The future of education in the health professions is clearly in the hands of the educators who prepare future health professionals.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
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 head0.348
GPT teacher head0.577
Teacher spread0.228 · 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
GenreReview

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

Citations12
Published2008
Admission routes1
Has abstractyes

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