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Record W2259010101 · doi:10.82308/14084

Medication administration complexity, work interruptions, and nurses' workload as predictors of medication administration errors

2009· article· en· W2259010101 on OpenAlexfundno aff
Alain Biron

Bibliographic record

VenueeScholarship@McGill (McGill) · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsnot available
FundersMcGill University
KeywordsWorkloadOvertimeMedicineOddsOdds ratioNursingLogistic regressionInternal medicineComputer science

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.376
Teacher spread0.310 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2009
Admission routes1
Has abstractyes

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