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Record W2016017787 · doi:10.3138/jvme.38.4.373

Case Study in Planning Revisions to a Veterinary Professional Curriculum: Opportunity, Motive, and Means

2011· article· en· W2016017787 on OpenAlexvenueaboutno aff
Lynne Olson, Stephen P. DiBartola

Bibliographic record

VenueJournal of Veterinary Medical Education · 2011
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMandateCurriculumMedical educationProcess (computing)Faculty developmentQuarter (Canadian coin)Political scienceCurriculum developmentState (computer science)Professional developmentPublic relationsMedicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

The Ohio State University (OSU) recently responded to a mandate from the state to convert from a quarter-based to a semester-based academic calendar. The OSU College of Veterinary Medicine took this opportunity to review and revise the curriculum leading to the DVM degree. This mandate occurred at a time when the college was motivated to act on recommendations that had been made during a recent reaccreditation process, some of which had been under discussion for several years, and had the personnel in place to initiate the change process. This article describes the means by which the curriculum change was planned. A review of the literature on change in health-sciences-related programs suggested that the ability to conclude the planning of changes in a relatively short time period was facilitated by adopting practices shown to promote successful curricular change. Critical aspects of the process included engaging the faculty, establishing a collective vision that entails agreement on principles, having a clear mandate and time frame for change, providing resources and training to support and sustain the change effort, and managing the effort centrally with groups that are broadly representative of the faculty.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.629
GPT teacher head0.590
Teacher spread0.039 · 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 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

Citations3
Published2011
Admission routes2
Has abstractyes

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