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Record W4390062910 · doi:10.3138/jvme-2023-0050

Knowledge and Perceptions of Antimicrobial Stewardship Concepts Among Final Year Veterinary Students in South Africa

2023· article· en· W4390062910 on OpenAlexvenueaboutno aff
Linè Fick, Lucille Crafford, Johan P. Schoeman, Natalie Schellack

Bibliographic record

VenueJournal of Veterinary Medical Education · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsnot available
Fundersnot available
KeywordsAntimicrobial stewardshipCurriculumVeterinary medicineMedicineStewardship (theology)Medical educationQuarter (Canadian coin)PerceptionFamily medicineNursingAntibiotic resistancePsychologyPolitical scienceGeographyPedagogy

Abstract

fetched live from OpenAlex

Antimicrobial resistance (AMR) has become a major global public health crisis due to inappropriate use in humans, animals, and crops. Studies to assess the knowledge and perceptions of antimicrobial stewardship (AMS) practices among medical and health care professionals have been conducted, yet this is the first among veterinary students in South Africa. A descriptive study surveyed 147 final year veterinary students at the Faculty of Veterinary Science, University of Pretoria. Of these, 102 completed the questionnaire (69% response rate). Most stated they knew what AMS was, while a minority heard of it for the first time. A small number understood poor hand washing could contribute to AMR. Almost a quarter of students stated their AMS knowledge was poor, and most noted a need for more training. The Bachelor of Veterinary Sciences curriculum should include more material on AMS and AMR to bridge training gaps.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.051
GPT teacher head0.373
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

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