Analysis of Statistical Knowledge of Peruvian Medical Students: A Cross-Sectional Analytical Study Based on a Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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 it