Low-fidelity simulation to enhance understanding of infection control among undergraduate medical students
Bibliographic record
Abstract
BACKGROUND: Developing economies are stressing institutional care for better health outcomes but its advantages are dampened by healthcare-associated infections (HAIs). Besides other undesired complications, the economic cost of HAIs is enormous. Developing countries have higher HAI rates compared with Europe or the USA. The knowledge and practice of infection control is poor among medical students. Based on introspection of 'Infection Control Module' for undergraduates introduced in 2012, we tested emotional sensitization using low-fidelity techniques to enhance its effectiveness. METHODS: All medical undergraduate students in their second year (n=102) were randomly divided into three groups using balanced randomization (two test and one control). Test groups were made to realize the emotional, social and financial consequences of HAI on patients and their families through low-fidelity simulation in the form of case discussions and video demonstrations. Pre- and postintervention empathy scores were calculated using Toronto empathy questionnaire (TEQ) for all the 102 students. Postintervention, all students were subjected to an infection control module and knowledge test. Perceptions of the intervention groups were recorded. Descriptive statistics and ANOVA were applied for data analysis. RESULTS: Of the 102 students, 93 (91.1%) participated in the study. There was no significant difference in the pre-test TEQ score (p=0.87) but there was a significant difference in the post-test TEQ (p = 0.026) and knowledge test score (p = 0.016) among the groups. Both the simulation exercises were well appreciated by the students. CONCLUSION: Emotional sensitization using low-fidelity simulation served as a catalyst in understanding infection control among medical undergraduate students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".