Knowledge gaps on HIV/AIDS among a group of nursing students in Sri Lanka
Bibliographic record
Abstract
Objective: Prevalence of HIV is low in Sri Lanka. However, the incidence is rising gradually. Reducing stigmatization and discrimination of people living with HIV is important in health care settings. Nurses who have an important role in caring for HIV patients should have good knowledge to achieve this. The aim of this study was to identify the specific areas of knowledge deficit on HIV/AIDS among 2nd year nursing student in Sri Lanka.Methods: In-depth analysis of the knowledge component of the pretest responses of a study assessing the effectiveness of AIDS education program on nursing students’ AIDS knowledge and AIDS attitudes in Sri Lanka.Results: The results show poor knowledge of HIV and important knowledge gaps in areas of modes of transmission of HIV, mother to child transmission and universal precautions. Very high percentage believed they are at higher risk of contracting HIV due to the nature of their job, while the knowledge on post-exposure prophylaxis was poor.Conclusions: Correction of these knowledge gaps and improving knowledge on HIV/AIDS among nursing students is very important as they are going to be future nurses and they have a very important role in reducing the discrimination and stigmatization of people living with HIV.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".