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Record W1999707608 · doi:10.3138/jvme.0511-049r1

Survey of Instructors Teaching about Antimicrobial Resistance in the Veterinary Professional Curriculum in the United States

2012· article· en· W1999707608 on OpenAlexvenueno aff
Virginia R. Fajt, H.M. Scott, William Alex McIntosh, Wesley R. Dean, Virginia Vincent

Bibliographic record

VenueJournal of Veterinary Medical Education · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMedical educationMedicineVeterinary medicineVeterinary public healthTeaching methodPublic healthPsychologyNursingPedagogy

Abstract

fetched live from OpenAlex

The objective of this study was to ascertain current teaching methods for antimicrobial resistance (AMR) in veterinary professional curricula and to find out what veterinary instructors consider to be prioritized subtopics related to AMR. The sampling frame was instructors in veterinary professional programs at US colleges of veterinary medicine who provide instruction about antibiotics or AMR in the disciplines of microbiology, pharmacology, public health, epidemiology, internal medicine, surgery, or related subjects. Identified instructors were invited to participate in an online survey of current teaching methods related to subtopics of AMR. From 1,207 invitations, 306 completed surveys were available for analysis (25% response rate) with the largest number of respondents stating their contact hours about antibiotics occur in the discipline of "medicine-food animal." The median contact time suggested for AMR in the core veterinary curriculum was 3-5 hours, and for antibiotics in general, 16-20 hours. Subtopics of AMR were prioritized based on respondents' indication that they use or would use various teaching tools. The most common teaching tool for all topics was projected text (i.e., slides or PowerPoint slides) and the least common were video clips, non-course Web sites, online modules, and laboratory experiments. Recommendations for identifying the priorities of AMR content coverage and learning outcomes are made.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.037
GPT teacher head0.354
Teacher spread0.317 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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