Multiplicity of medication safety terms, definitions and functional meanings: when is enough enough?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it