Using Human Factors Methods to Evaluate the Labelling of Injectable Drugs
Bibliographic record
Abstract
Adverse drug events, including in-hospital medication errors, are a well-documented world-wide problem. This interdisciplinary team set out to examine the issues related to the labelling of injectable drugs. We sought answers to the following two questions: (1) To what extent do injectable drug labels adhere to existing Canadian design practice recommendations and regulations for labelling and (2) is there a need to make changes to the recommendations or regulations for labelling of injectable drugs in Canada? The project contained three phases. The first phase involved taking a sample of vials and ampoules from a hospital pharmacy and identifying adherence to the 1999 Canadian Standards Association standard for the labelling of drug ampoules, vials and prefilled syringes, as well as with the Canadian (Health Canada) Food and Drug Regulations for labelling. The second phase involved a failure mode and effects analysis of the label-reading process in order to identify information on the label considered critical for safe medication use. The third phase involved a preliminary human factors experiment addressing one problem identified with existing labels. Our finding is that existing injectable drug labels do not adhere sufficiently to available best design standards for labels and also do not adhere to all Canadian Food and Drug Regulations. Recommendations are made to inform future enhancements to labelling standards, guidelines and regulations.
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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 | 0.075 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".