Initial Validation of a Survey Instrument to Evaluate Veterinary Student Self-Efficacy for Antimicrobial Selection in the United States
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a major threat to human and animal health, and antimicrobial use selects for AMR. Appropriate selection of antimicrobial drugs is an important part of veterinary education, but many veterinary students report that they have knowledge gaps in this area. Students with greater self-efficacy, the belief that one can perform the individual steps that comprise a task, tend to expend more effort and motivation in learning new skills. Educational activities that improve self-efficacy can increase student motivation, but appropriate assessment tools tailored for specific educational domains are necessary to support these efforts. The purpose of this study was to validate an online survey instrument to measure veterinary student self-efficacy for antimicrobial selection. The secondary goal was to determine if clinical training increases veterinary students' self-efficacy for antimicrobial selection. A total of 380 students from seven veterinary colleges in the United States completed an online survey instrument that asked students to self-assess their abilities to perform 13 tasks associated with antimicrobial selection on a 10-point Likert-type scale. A principal components analysis identified three factors associated with self-efficacy for antimicrobial selection: (a) empirical selection and dosing of antimicrobials, (b) identification of trustworthy resources and resistance to pressure to prescribe, and (c) knowledge of when antimicrobials are needed. Self-efficacy for antimicrobial selection increases the most in the fourth year of veterinary training. However, exposure to at least one clinical rotation was not associated with higher self-efficacy for selection of antimicrobials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".