Medication administration complexity, work interruptions, and nurses' workload as predictors of medication administration errors
Bibliographic record
Abstract
Background: The evidence to date in support of system related factors to account for medication administration errors (MAE) remains scant and inconclusive. Objective: To examine the predictive power of medication administration complexity (component and coordinative), work interruptions and nurses' workload as potential contributing factors to MAE. Design: A prospective correlational design. Setting: A medical patient care unit in a university teaching hospital Sample: A convenience sample of medication administration rounds performed by registered nurses with at least six months of professional experience. Method: Data were collected using direct observation (MAE and work interruptions), self-report measures (subjective workload, nurses' characteristics) and the Medication Administration Complexity (MAC) coding scale (component and coordinative medication complexity). Results: One hundred and two rounds were observed, during which 965 doses were administered and performed by 18 nurses. When wrong administration time errors were included, MAE rate was 28.4% whereas it decreased to 11.1% when wrong time errors were excluded. An interruption during the medication preparation phase (OR 1.596; 1.044 - 2.441) significantly increased the odds of MAE. Two significant interaction effects were found (patient demand for nursing care X overtime and patient demand for nursing care X professional experience). These interactions pointed to more negative effects of overtime and professional experience among nurses who rated the demand for nursing care as above average. Contrary to expectations, coordinative medication administration complexity significantly decreased the odds of MAE (OR 0.558; .322-.967). Including wrong administration time errors changed the cluster of predictors with component medication administration complexity (1.039; 1.016 - 1.062), and nurses' workload (1.221; 1.061 - 1.405) were significant pre
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".