Improving Antimicrobial Resistance Awareness Among Medical Students in India: The Sensitization of Medical Students on Antimicrobial Resistance (SOS-AMR) Study
Bibliographic record
Abstract
OBJECTIVES To evaluate the impact of an online educational intervention on improving knowledge of antimicrobial resistance (AMR) and stewardship among final-year medical students in Chennai, India. METHODS This was a prospective ‘before-after’ study conducted across 5 medical colleges in Chennai, India. Participants who were final-year (fourth year) undergraduate medical students were administered a pretest to evaluate baseline knowledge. Students were then provided access to online educational material comprising 20 short lectures. Lectures were delivered by content experts and covered a range of topics which included basics of microbiology, fundamental concepts in AMR and stewardship, diagnosis and management of common infections, basics of antimicrobial pharmacokinetics and pharmacodynamics, and vaccination. Students were required to take a posttest at the end of these modules. Primary outcome was improvement in test scores from pretest baseline which was analyzed using a t test. A 30% improvement in the mean scores from baseline was predefined as a measure of success. RESULTS A total of 599 students participated from 5 medical colleges among whom 339 (56.6%) were female participants; 542 (90.4%) students completed the posttest. Mean pretest score was 11.6 (maximum possible score of 25) (SD: 4.3) and the mean posttest score was 14.0 (SD: 4.6). Comparing pre and posttest scores, there was an improvement of 2.4 marks (20%) from the baseline (95% confidence interval: 1.9, 2.9) ( P < .001). Improvement in scores was similar for male and female participants. CONCLUSIONS In this before-after study evaluating the impact of an educational intervention on AMR among final-year medical students, there was an improvement in knowledge; however, the extent of improvement did not meet the predefined metric of success.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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".