Effect of Different Instructional Methods on Contamination and Personal Protective Equipment Protocol Adherence among Veterinary Students
Bibliographic record
Abstract
Proper use of personal protective equipment (PPE) is crucial to prevent disease spread. Recent studies in human medicine have shown disconcerting inconsistencies in the use of PPE in hospital wards. In this study, we compared the effect of three instructional methods for PPE use on contamination and protocol adherence among veterinary students. Students were divided into three groups according to the instructional method to which they had access (instructional video, wall chart, or both). They underwent an isolation exercise consisting of donning, patient examination (mock patient prepared with contamination marker), and doffing. Student contamination after the exercise was evaluated using UV light. Videos of student performance were reviewed for errors committed. Results showed that the number of students with contamination was higher in the group who only had access to video instruction than in the two other groups. The number of students with contamination on forearms, hands, and wrists was higher in the group who only had access to charts. Disinfecting gloves between doffing steps was the most frequently omitted step. The number of students who touched the environment with unprotected areas of their bodies was higher in the group who only had access to video instruction than in the other two groups. In conclusion, video instruction was less effective in achieving PPE protocol adherence among veterinary students than was instruction with a chart or chart-video combination. Incorporating video instruction as part of the instructions may be valuable to reinforce individual steps of donning and doffing.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".