Long‐term care physical environments – effect on medication errors
Bibliographic record
Abstract
PURPOSE: Few studies examine physical environmental factors and their effects on staff health, effectiveness, work errors and job satisfaction. To address this gap, this study aims to examine environmental features and their role in medication and nursing errors in long-term care facilities. DESIGN/METHODOLOGY/APPROACH: A mixed methodological strategy was used. Data were collected via focus groups, observing medication preparation and administration, and a nursing staff survey in four facilities. FINDINGS: The paper reveals that, during the medication preparation phase, physical design, such as medication room layout, is a major source of potential errors. During medication administration, social environment is more likely to contribute to errors. Interruptions, noise and staff shortages were particular problems. RESEARCH LIMITATIONS/IMPLICATIONS: The survey's relatively small sample size needs to be considered when interpreting the findings. Also, actual error data could not be included as existing records were incomplete. PRACTICAL IMPLICATIONS: The study offers several relatively low-cost recommendations to help staff reduce medication errors. Physical environmental factors are important when addressing measures to reduce errors. ORIGINALITY/VALUE: The findings of this study underscore the fact that the physical environment's influence on the possibility of medication errors is often neglected. This study contributes to the scarce empirical literature examining the relationship between physical design and patient safety.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".