Effect of educational intervention on healthcare providers knowledge and perception towards pharmacovigilance: A tertiary teaching hospital experience
Bibliographic record
Abstract
OBJECTIVE: Based on the theory on planned behavior, perception or attitude is found to be a well-established predictor of healthcare providers' intentions to perform different behaviors. Also, improving knowledge was proposed to affect their practice as well. In Jordan, many studies have been conducted to evaluate healthcare providers' knowledge and perception towards pharmacovigilance but no intervention or training was provided. Thus, the aim of this study was to evaluate the impact of an educational workshop on the knowledge and perception of healthcare providers towards pharmacovigilance in a Jordanian tertiary teaching hospital. METHODS: An interventional study conducted in Jordan University Hospital on various healthcare providers to assess their pre- and post-knowledge and perception towards pharmacovigilance and adverse drug reactions (ADRs) reporting via questionnaire before and after an educational workshop. RESULTS: Among the 200 invited healthcare providers, 150 attended the educational workshop (response rate 75.0%). Pre-workshop, healthcare providers showed an overall low knowledge score (7.8/19), where only 8.7% could define pharmacovigilance correctly. On the other hand, they showed a favorable perception score (33.6/39).Following educational workshop, knowledge scores significantly improved by 67.9% (P-value <0.05). A similar finding was obtained for perception scores, where perception scores significantly improved by 10.1% following workshop (P-value <0.05). CONCLUSION: Continuous efforts are needed to implement different strategies including education modules and the provision of appropriate training programs to increase awareness and improve perception towards pharmacovigilance among healthcare providers. Future study is needed to evaluate the impact of improving knowledge and perception on ADRs reporting practice.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".