Percentage of Injuries, and Related Factors Among a Group of Medical Students in Cairo University: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: There has been a lack of data on injuries in young adults, including university students, in particular medical students. AIM: The current study was conducted to explore the percentage, and related factors of injuries among a group of medical students, who constitute an important risk group for accidents and injuries. METHODS: This exploratory cross-sectional study was conducted in Kasr Al-ainy Medical School. A convenient sample of medical students was chosen. 1300 survey questionnaires were distributed along all academic years, 807 from them were completely retrieved. The questionnaire form was adopted from “Health behavior survey among university students in low- and middle-income countries questionnaire.” Data entry and analysis were carried out using SPSS 21.0. RESULTS: One-quarter reported having a form of injury last year. Falling was the most nominated cause of injuries by the participant students (5,31.3%). Only those who “perceived” their general health as “well” reported being injured significantly more than those who perceived their general health as poor, with a p value = 0.006 and odds ratio of 1.6 (1.1–2.2). Taking drugs was the only factor significantly determining how the injury happened; whether it is intentional or unintentional with a p = 0.01 highlighting that about one-third (5, 31.3%) of those who were intentionally injured were taking drugs. CONCLUSION: Two factors were identified which will increase the understanding of public health of injuries in university communities to design programs for injury prevention programs specifically targeting medical students.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".