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Record W2753630777 · doi:10.3138/jvme.1116-174

A Novel Model for Teaching Primary Care in a Community Practice Setting: Tufts at Tech Community Veterinary Clinic

2017· article· en· W2753630777 on OpenAlexvenueno aff
Emily McCobb, Elizabeth A. Rozanski, Elizabeth L. Malcolm, Gregory Wolfus, John E. Rush

Bibliographic record

VenueJournal of Veterinary Medical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePrimary careVeterinary medicineGeneral partnershipMedical educationPopulationNursingFamily medicine

Abstract

fetched live from OpenAlex

Providing veterinary students with opportunities to develop clinical skills in a realistic, hands-on environment remains a challenge for veterinary education. We have developed a novel approach to teaching clinical medicine to fourth-year veterinary students and technical high school students via development of a primary care clinic embedded within a technical high school. The primary care clinic targets an underserved area of the community, which includes many of the participating high school students. Support from the veterinary community for the project has been strong as a result of communication, the opportunity for veterinarians to volunteer in the clinic, and the careful targeting of services. Benefits to veterinary students include the opportunity to build clinical competencies and confidence, as well as the exposure to a diverse client population. The financial model of the clinic is described and initial data on outcomes for case load, clinic income, veterinary student evaluations, and high school students' success in passing the veterinary assisting examination are reported. This clinical model, involving a partnership between a veterinary school and a technical high school, may be adoptable to other clinical teaching situations.

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)0.014
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.006
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 head0.533
GPT teacher head0.609
Teacher spread0.075 · 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 designQualitative
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

Citations21
Published2017
Admission routes1
Has abstractyes

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