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Record W2170554898 · doi:10.3138/jvme.36.1.114

An Innovative Approach to Post-graduate Education in Veterinary Public Health

2009· article· en· W2170554898 on OpenAlexvenueno aff
Jenny‐Ann Toribio, Hannah Forsyth, R. R. Laxton, Richard J. Whittington

Bibliographic record

VenueJournal of Veterinary Medical Education · 2009
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
FundersVincent Fairfax Family Foundation
KeywordsFacilitatorVeterinary public healthPublic healthMedical educationModalitiesDistance educationVeterinary medicineMedicinePsychologyNursingSociologyPedagogy

Abstract

fetched live from OpenAlex

The past decade has seen a substantially increased need for animal health professionals who have advanced education in areas that impact on veterinary public health (VPH). The University of Sydney has made a significant contribution to the international capacity for training in this field by developing an online, distance program in Veterinary Public Health Management. This paper describes the distinctive characteristics of this program, which combines technical material in a range of units that influence VPH with leadership and project management. It then describes the educational model developed for delivery of its course material, including the four modalities that are structured to support engaged learning by busy animal health professionals who are working full-time (self-led, facilitator-led, peer-led, and assessment-led instructional approaches). Finally, having reflected on the efficacy of this model for post-graduate training in VPH, we discuss the progress of the program since its inception in 2002, reflecting on the challenges it has encountered and defining the factors that are critical to the success of this program.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.556
GPT teacher head0.593
Teacher spread0.037 · 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 designOther design
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

Citations1
Published2009
Admission routes1
Has abstractyes

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