Experiences of a nationwide web-based system: reporting dispensing errors in Swedish pharmacies
Bibliographic record
Abstract
OBJECTIVES: To design and evaluate a national web-based dispensing error reporting system for all Swedish pharmacies, replacing the currently used paper-based system. METHODS: A working group designed the new system. The number of reports before (1999-2003) and after (2004-2005) introduction was studied in a descriptive analysis. The completeness of reports was evaluated through the study of 100 randomly selected reports from the third quarter of 2003 and 2004 from each system. Evaluation was done by chi-square analysis; P>0.05. Perceptions on introduction were collected in semi-structured interviews (working group and one assistant) and subjected to descriptive analysis. KEY FINDINGS: Reported error rate per 100,000 dispensed items was 12.9 pre- and 21.4 post implementation. Completeness-analysis revealed that information was more comprehensively reported in the new system. A significant difference existed in the extent to which incidents were described as well as details provided of the medicine and the patient. According to the interviewees, users initially found the web-based system difficult to handle. It took more than 6 months to change this perception. CONCLUSIONS: Introducing a web-based system for reporting dispensing errors had an impact on quantity of reports and completeness. Time and patience was needed to implement the changes.
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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 | 0.027 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".