Going blank: factors contributing to interruptions to nurses’ work and related outcomes
Bibliographic record
Abstract
AIM: To examine interruptions to nurses' work, the systems issues related to these and the associated outcomes. BACKGROUND: While some research has described the role interruptions play in medication errors, work is needed to examine specific factors in the nursing work environment that cause interruptions and to assess the impact of these on nurses' work and patient outcomes. METHODS: The present study utilized a mixed method design that involved work observation to detect nursing interruptions in the workplace followed by focus groups with a subsample of nurses. RESULTS: A total of 13,025 interruptions were observed. Equal numbers of these took place on medical and surgical units. The predominant source of interruptions was members of the health team, who interrupted more frequently on medical units. CONCLUSIONS: Differences in the type of patient and the care needs between medical and surgical units may be a contributing factor to these findings. As members of the health team were among the leading source of interruptions, an interdisciplinary team-based approach to changing the organization and design of work should be explored. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse leaders should examine ways in which nurses' work can benefit from system improvements to reduce interruptions that lead to patient safety issues such as treatment delays and loss of concentration.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".