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Record W2769770323 · doi:10.3138/jvme.1116-181r1

Milestone Educational Planning Initiatives in Veterinary Medical Education: Progress and Pitfalls

2017· article· en· W2769770323 on OpenAlexaffvenueabout
Elizabeth A. Stone, Jessica Reimann, Lisa M. Greenhill, Cate Dewey

Bibliographic record

VenueJournal of Veterinary Medical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMilestoneFutures studiesTracking (education)Student debtMedical educationPlan (archaeology)Strategic planningGovernment (linguistics)MedicineDiversity (politics)Political scienceVeterinary medicineHigher educationPsychologyManagementPedagogy

Abstract

fetched live from OpenAlex

Three milestone educational planning initiatives engaged the veterinary medical profession in the United States and Canada between 1987 and 2011, namely the Pew National Veterinary Education Program, the Foresight Project, and the North American Veterinary Medical Education Consortium. In a quantitative study, we investigated the impact of these initiatives on veterinary medical education through a survey of academic leaders (deans, previous deans, and associate deans for academics from veterinary medical schools that are members of the Association of American Veterinary Medical Colleges) to assess their perspectives on the initiatives and eight recommendations that were common to all three initiatives. Two of the recommendations have in effect been implemented: enable students to elect in-depth instruction and experience within a practice theme or discipline area (tracking), and increase the number of graduating veterinarians. For three of the recommendations, awareness of the issues has increased but substantial progress has not been made: promote diversity in the veterinary profession, develop a plan to reduce student debt, and develop a North American strategic plan. Lastly, three recommendations have not been accomplished: emphasize use of information more than fact recall, share educational resources to enable a cost-effective education, and standardize core admissions requirements. The educational planning initiatives did provide collaborative opportunities to discuss and determine what needs to change within veterinary medical education. Future initiatives should explore how to avoid and overcome obstacles to successful implementation.

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.398
GPT teacher head0.599
Teacher spread0.201 · 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

Citations7
Published2017
Admission routes3
Has abstractyes

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