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Record W2029068163 · doi:10.2146/ajhp120344

Identifying and reducing distractions and interruptions in a pharmacy department

2013· article· en· W2029068163 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Health-System Pharmacy · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPharmacyEmergency departmentPsychologyMedicineNursingPsychiatry

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.288
GPT teacher head0.508
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it