Developing a Medication Administration Observation Checklist
Bibliographic record
Abstract
BACKGROUND: Medication administration incidents are a significant patient safety concern in health care, often driven by human and work system factors which contribute to errors. PURPOSE: The purpose of this study was to develop a medication administration observation checklist tool tailored for a pediatric tertiary care center in Atlantic Canada. METHODS: We synthesized existing evidence on methodologies for observational tools in medication administration by nurses. Next, we engaged nursing knowledge users in Think Aloud sessions to iteratively refine the checklist's items. RESULTS: We share the development of a medication administration observation checklist tool. This process incorporated valuable feedback from frontline nurses and nurse managers, ensuring the checklist's relevance and usability in clinical practice. CONCLUSIONS: Our findings underscore the importance of co-developing data collection tools with interdisciplinary teams, leveraging theoretical frameworks to capture complexities in workflow, and enhancing transparency in reporting methodologies to support replicability across diverse clinical contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".