Nursing Students' Knowledge Regarding Sexually Transmitted Diseases
Bibliographic record
Abstract
Background: Sexually transmitted infections are a severe health issue. Every year, more than 333 million new cases are recorded around the world, with adolescents being the most typically affected demographic. Objectives: To identify the level of nursing students' knowledge regarding sexually transmitted diseases. Methods: A descriptive study design was carried out using an assessment approach from November 12th, 2024, to May 25th, 2025. A non-probability (purposive) sample of 208 nursing students was selected. The questionnaire was designed to identify nursing students' knowledge regarding sexually transmitted diseases in two parts: demographic data (4 items) and nursing students' knowledge regarding sexually transmitted diseases (27 items). Results: The study showed that less than half (48.38%) reported a fair level for the knowledge, less than one third a poor level (27.95%), and less than one quarter a good level (23.65 %).While demonstrating that the overall student knowledge has a significant link with their department, class, at a p-value less than 0.05. Conclusion: The researcher concludes that a significant disparity in students' understanding, highlighting the need for educational development at all levels. Furthermore, statistical analysis revealed a strong correlation between students' general knowledge levels and their department and class (p < 0.05), indicating that academic discipline and year of study play a vital role in knowledge development. Highlights: Less than half of the students had only a fair understanding of STDs. Academic department and class level significantly influenced knowledge. Highlights the urgent need for curriculum-based education interventions. Keywords: Sexually Transmitted Diseases, Nursing Students, Knowledge Level, Descriptive Study, Health Education
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".