Analysis of Statistical Knowledge of Peruvian Medical Students: A Cross-Sectional Analytical Study Based on a Survey
Bibliographic record
Abstract
Introduction: Despite the growing awareness of the importance of knowledge in biostatistics, many investigations worldwide have found that medical students have a poor understanding of it. Objective: To determine the percentage of Peruvian medical students with sufficient biostatistics knowledge and the associated factors. Methods: Cross-sectional analytical study. Application of a virtual survey to medical students from different faculties in Peru. Results: 56.46% of medical students have insufficient knowledge of biostatistics. A statistically significant association was found for those who were 25 years of age or older (aPR: 1.195; 95% CI 1.045 - 1.366; p=0.009); being between the 9th and 12th semester (aPR: 1.177; 95% CI 1.001 - 1.378; p=0.037) and medical internship (aPR: 1.373; 95% CI 1.104 - 1.707; p=0.004); take an external course in biostatistics, epidemiology or research (aPR: 4.016; 95% CI 3.438 - 4.693; p<0.001); having read more than 12 articles per year (aPR: 1.590; 95% CI 1.313 - 1.967; p<0.001); and publish at least one scientific article (aPR: 1.549; 95% CI 1.321 - 1.816; p<0.001) or more than one (PR: 2.312; 95% CI 1.832 - 2.919; p<0.001). Conclusions: There is insufficient knowledge of biostatistics in medical students. The factors associated with a good understanding of this were age, academic semester, the number of articles read and published, and having taken an external course.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".