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Record W2105481511 · doi:10.3138/jvme.35.2.219

Public Health Education at the University of California, Davis: Past, Present, and Future Programs

2008· article· en· W2105481511 on OpenAlexvenueno aff
David W. Hird, K. C. Kent Lloyd, Stephen A. McCurdy, Marc B. Schenker, John J. Troidl, Philip H. Kass

Bibliographic record

VenueJournal of Veterinary Medical Education · 2008
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthVeterinary public healthEconomic shortageMedical educationPreventive healthcareMedicineVeterinary medicinePolitical scienceNursingGovernment (linguistics)

Abstract

fetched live from OpenAlex

This article reviews the history of public-health education at the University of California, Davis, from the inception of the Master of Preventive Veterinary Medicine Program in the School of Veterinary Medicine through the creation of the Master of Public Health Program offered jointly by the Schools of Medicine and Veterinary Medicine. The long history of collaborative teaching and research between the schools, as well as the university's close proximity to and relationship with numerous university-affiliated and state public-health agencies, has created remarkable opportunities for novel and creative public-health education. The university is already anticipating the approval of a School of Public Health on its campus, which will create even more educational opportunities in both human and veterinary public-health disciplines. Given the projected shortfall of veterinarians entering such fields, the opportunity of a novel Doctor of Public Health degree program specifically suited to the needs of veterinary medicine is also discussed as a means of addressing this shortage.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.414
GPT teacher head0.488
Teacher spread0.073 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2008
Admission routes1
Has abstractyes

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