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Record W3193024795 · doi:10.3138/jvme-2021-0044

Assessing Backyard Poultry versus Small Animal Knowledge of Veterinary Students regarding Husbandry, Prescription Drug Use, and Antimicrobial Resistance

2021· article· en· W3193024795 on OpenAlexvenueno aff
Myrna Cadena, Todd Kelman, Maurice Pitesky, Rachel Dutch, Lisa A. Tell

Bibliographic record

VenueJournal of Veterinary Medical Education · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVeterinary medicineAnimal husbandryVeterinary drugLikert scaleMedical prescriptionNursingPsychologyAgricultureBiology

Abstract

fetched live from OpenAlex

As backyard poultry (BYP) ownership has increased in the US, the demand for veterinarians who treat BYP has also increased. However, veterinarians who treat BYP remain scarce and are mostly small animal veterinarians and exotic animal practitioners who have limited training in food animal regulatory practices. To gauge whether veterinary students are interested in learning more about BYP and what BYP topics to include in an online training program for veterinary students, a BYP knowledge assessment was conducted. Pre-clinical veterinary students were asked to self-rate their level of knowledge on various topics for both small animal practice and BYP using Likert-type ordinal scales. Wilcoxon signed-rank tests of those Likert data showed significant differences ( p < .01 at α = .05) between self-assessed knowledge of poultry and small animal medicine for all surveyed topics. Specifically, veterinary students ranked themselves as less knowledgeable on poultry concepts than on small animal medicine concepts. Nevertheless, students expressed interest in an online training program for treating BYP and drug residue avoidance in BYP, despite having chosen future career tracks that are not exclusively poultry. Specific topics students expressed interest in with respect to BYP training included anatomy, husbandry, prescribing medications, treatment options, food safety, antimicrobial resistance, and extra-label drug use.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.166
GPT teacher head0.384
Teacher spread0.218 · 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 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
Published2021
Admission routes1
Has abstractyes

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