Pharmacovigilance: Awareness and Practice of Nurses and Midwives in Monitoring and Reporting Adverse Drug Reactions in a Selected University Teaching Hospital, Rwanda
Bibliographic record
Abstract
Background: Adverse drug reactions result in thousands of deaths, disabilities, and other serious outcomes. Nurses and midwives administer drugs, monitor both therapeutic and adverse drug reactions, and are on the front line of safety reporting. This study aimed to assess awareness of nurses and midwives about pharmacovigilance and their practice in monitoring and reporting adverse drug reactions at the University Teaching Hospital of Kigali. Methods: We conducted a cross-sectional study on 147 randomly selected nurses and midwives. Self-administered questionnaires were used to collect data. We analyzed data using SPSS version 22 computer software for descriptive and inferential statistics. Results: Concerning the awareness of nurses and midwives, 88% had heard about pharmacovigilance, and 22.3% were aware of Rwanda Food and Drug Authority. Nearly two-thirds (62.3%) reported inadequate practice in monitoring adverse drug reactions. Their practice was associated with having heard about pharmacovigilance (p=0.004) and knowing the hospital's adverse drug reactions reporting system (p=0.005). Concerning practice in reporting adverse drug reactions, 66.2% had observed adverse drug reactions, and 18.2% filled out adverse event notification forms. Conclusion: Few nurses and midwives were aware of the pharmacovigilance system in Rwanda, and many of them reported inadequate practices toward monitoring and reporting adverse drug reactions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".