A Multicenter Evaluation of a Metacognitive Framework for Antimicrobial Selection Education
Bibliographic record
Abstract
Antimicrobial selection is a complex task for veterinary students there is a need for both assessment tools and novel strategies to promote the proper use of antimicrobials. SODAPOP (Source-Organism-Decided to treat-Antimicrobials-Patient-Option-Plan) is a mnemonic previously designed to aid in developing antimicrobial selection skills by promoting metacognition. To assess the effect of this tool, we enrolled veterinary students ( N = 238) from five veterinary teaching institutions in a study that consisted of an online survey that contained a video-based intervention. For the intervention, a video that presented principles of antimicrobial selection was embedded within the survey. For one-half of students, the video also included an explanation of SODAPOP. The survey included self-efficacy statements rated by participants pre-intervention and post-intervention. The survey also included cases, developed for this study, that were used to assess selection and plan competence. Cases were graded using two study-developed rubrics in a blinded fashion by veterinary educators. A statistically significant difference was found in participant-reported self-efficacy pre-scores and post-scores when asked about empiric prescribing (5.8 vs. 6.5; p = .0153) for the SODAPOP group but not the control group. No immediate impact on competence was found. When asked whether SODAPOP was an essential educational tool and likely to be used by participants in the future, the mean rank score (from 1-10) was 7.6 and 7.2, respectively. In addition to developing cases and rubrics, this study demonstrated that SODAPOP may be a useful tool for integration into approaches for teaching antimicrobial selection to veterinary students.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".