Identifying and reducing distractions and interruptions in a pharmacy department
Bibliographic record
Abstract
Interruptions are an acknowledged problem in health care systems.1 Health care workers are exposed to various types of stimuli that may cause distractions or interruptions. Interruptions are a major concern in the hospital pharmacy setting, given the nature and the requirements of the work. Sustained and focused attention is required for validating prescriptions and performing other complex processes. Interruptions may jeopardize the safe delivery of pharmaceutical services. It is recognized that stimuli resulting in distractions and interruptions can increase a person’s stress, discomfort, and dissatisfaction and can have an overall negative impact on ergonomics.2 It is also recognized that distractions and interruptions can impair work processes. Health care providers need to maintain a high level of attention to complete tasks effciently. Sinclair et al.3 demonstrated that a group of pharmacists and pharmacy technicians took an average of 27% more time to perform a standard pharmacy validation task (checking the accuracy of noncomplex prescriptions) when they were interrupted, relative to the time it took to perform similar validation tasks without being interrupted.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".