Development of Canadian Safety Indicators for Medication Use
Bibliographic record
Abstract
Reports of preventable illness due to medication errors are widespread in Canada. However, quantifying the magnitude of the problem has been hampered by a lack of measurement tools. Canadian-specific indicators, or performance measures, of safe medication use do not exist. The objective of this study was to develop a set of Canadian consensus-based indicators for the safe use of medication for both in-patient and outpatient settings. A panel of 20 national experts was established from a convenience sample of experts representing medicine, nursing, pharmacy, research and decision-makers in hospitals and community settings across Canada. After creating a list of potential indicators from the literature, the final consensus set was chosen by the panel using a Delphi survey process via e-mail. After three rounds, consensus was obtained on 20 medication-use safety indicators: seven indicators were related to systems of care, five to prescribing/ordering, three to monitoring/assessment, three to medication administration, one to preparation and dispensing and one to purchasing/inventory management. Seventeen of the indicators measure a process of care (in contrast to health outcome); at least 10 have applications outside the in-patient setting. The resulting 20 medication-use safety indicators are diverse in scope and should be applicable in a variety of practice settings. These indicators may provide clinicians and decision-makers with valuable tools to assess the safety of medication-use systems.
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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 | 0.029 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".