Medication Error Disclosure and Attitudes to Reporting by Healthcare Professionals in a Sub-Saharan African Setting: A Survey in Uganda
Bibliographic record
Abstract
BACKGROUND: Medication errors (MEs) are largely under-reported, which undermines quality improvement and medication risk management in healthcare. OBJECTIVES: To assess attitudes of Ugandan healthcare professionals (HCPs) towards ME reporting, and identify characteristics of HCPs who endorsed integration of ME and adverse drug reaction (ADR) reporting, valued patient involvement in ME reporting, disclosed having ever made potentially harmful MEs, or observed possibly harmful MEs committed by other HCPs. METHODS: Healthcare professionals self-completed a questionnaire on their attitudes towards the occurrence and reporting of MEs in purposively selected Ugandan health facilities (public/private) including the national referral and six regional referral hospitals representative of all regions. RESULTS: Response rate was 67 % (1345/2000). Most HCPs (91 %; 1174/1289) approved a national ME reporting system for Uganda and 58 % (734/1261) endorsed integration of ME and ADR reporting. Two-thirds (65 %; 819/1267) of HCPs valued patient involvement in ME reporting, one-fifth (18 %; 235/1310) disclosed that they had ever made potentially harmful MEs, while two-fifths (41 %; 542/1323) had ever identified possibly harmful MEs committed by other HCPs. Endorsing patient involvement in ME reporting was more likely by HCPs who valued root-cause analysis and reporting of both actual and potential MEs, or who conceded inadequate communication and lack of time. Self-disclosure of having ever committed potentially harmful MEs was more likely with the need for confidentiality, working in stressful conditions, and willingness to report ADRs. Identifying possibly harmful MEs committed by other HCPs was more likely by non-nurses and those who reported blame culture, stressful conditions, ever encountered a fatal ADR, or attachment to hospital-level health facility. CONCLUSION: A non-punitive healthcare environment and patient involvement may promote ME disclosure and reporting in Uganda and possibly other African countries.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".