Reporting on Adverse Drug Reactions: Knowledge, Attitudes and Practice Among Physicians Working at Healthcare Institutions in Al-Buraimi Governorate-Oman
Bibliographic record
Abstract
OBJECTIVE: The study aims to evaluate postgraduate resident physicians’ knowledge, attitudes and practices related to reporting adverse drug reactions (ADRs). It also aims to investigate the causes of poor ADR reporting and to suggest possible ways to improve the reporting methods.METHODS: A cross-sectional study was conducted using a self-administered questionnaire. The questionnaire sought to obtain the physicians’ demographic characteristics, knowledge and practices in relation to ADRs and to identify the factors that affect and encourage ADR reporting. The questionnaire was distributed to physicians (n=117) working at governmental healthcare institutions in Al-Buraimi governorate in Oman.RESULTS: The response rate was 80%. Median score for the knowledge components of ADR reporting was 5 (total score: 7); it was 5 (total score: 5) for the attitude components. No significant difference for the knowledge and attitude scores was found between gender, age group or physicians’ medical speciality. Eighty-four of the physicians (89.4%) knew about pharmacovigilance and serious ADRs. Eighty-eight of the physicians (93.6%) believed that reporting ADRs should be mandatory. No statistical differences were found between general practitioners and specialists who felt that ADR reporting should be either compulsory or voluntary (p=0.080). Seventy-eight of the physicians (83%) noted that the lack of awareness about the reporting procedures is the main reason for not reporting ADRs. In this regard, there were no statistically significant differences between physicians younger than 45 or older than 45 (p=0.835).CONCLUSION: Deficits in the practice of ADR reporting can be resolved in the future only if all physicians in the healthcare profession are aware of the importance of reporting ADRs, the reporting system and their obligation to report ADRs.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".