Characteristics of Work Interruptions During Medication Administration
Bibliographic record
Abstract
OBJECTIVE: To document characteristics of nurses' work interruptions (WIs) during medication administration. DESIGN: A descriptive observational study design was used along with a sample of 102 medication administration rounds. Data were collected on a single medical unit using a unit dose distribution system during fall 2007. METHOD: Data collection on WIs relied on direct structured observation. The following WI characteristics were recorded: source, secondary task, location, management strategies, and duration. RESULTS: 374 WIs were observed over 59 hours 2 minutes of medication administration time (6.3 WI/hr). During the preparation phase, nurse colleagues (n= 36; 29.3%) followed by system failures such as missing medication or equipment (n= 28; 22.8%) were the most frequent source of WIs. Nurses were interrupted during the preparation phase mostly to solve system failures (n= 33; 26.8%) or for care coordination (n= 30; 24.4%). During the administration phase, the most frequent sources of WIs were self-initiation (n= 41; 16.9%) and patients (n= 39; 16.0%). The most frequent secondary task undertaken during the administration phase was direct patient care (n= 105; 43.9%). WIs lasted 1 min 32 s on average, and were mostly handled immediately (n= 357; 98.3%). CONCLUSIONS: The process of medication administration is not protected against WIs, which poses significant risks. CLINICAL RELEVANCE: Interventions to reduce WIs during the medication administration process should target nurses and system failures to maximize medication administration safety.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".