Multiplicity of medication safety terms, definitions and functional meanings: when is enough enough?
Bibliographic record
Abstract
OBJECTIVES: To identify the terms and definitions used by organisations involved in medication safety and to examine differences in functional meaning using a novel scenario assignment method. METHODS: Medication safety related terms and definitions were sought from websites of organisations associated with medication safety. The functional meanings of terms and definitions were analysed and compared using a scenario assignment method where each definition found was assessed against four scenarios with a central theme. MAIN OUTCOME MEASURES: Medication safety related terms and definitions currently in use, similarities and differences in their functional meanings, and practical implications of the use of these terms and definitions. RESULTS: Thirty three of 160 websites searched were found to have one or more definitions for medication safety related terms. Twenty five different terms with 119 definitions were found. The most frequently defined groups of terms were "adverse event" (8 different definitions), "error" (n = 9), "near miss" (n = 12), "adverse reaction" (n = 8), and "incident" (n = 4). Substantial diversity of functional meanings of definitions was demonstrated using the scenario-assignment method. Of the five groups of frequently defined terms, definitions within the "adverse event", "near miss", and "incident" groups resulted in three functional meanings each, while two functional meanings resulted for "error" and "adverse reaction". CONCLUSION: The multiplicity of terms, definitions and, most importantly, functional meanings demonstrates the urgent need for agreement on standardisation of nomenclature describing medication related occurrences. This is an essential prerequisite to enable meaningful analysis of incidence data and development of medication safety improvement strategies.
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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 | 0.099 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.012 | 0.037 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".