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Record W2128387232 · doi:10.1111/nup.12022

Beat the clock! Wait times and the production of ‘quality’ in emergency departments

2013· article· en· W2128387232 on OpenAlexaffabout
Karen Melon, Deborah White, Janet Rankin

Bibliographic record

VenueNursing Philosophy · 2013
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsTriageRestructuringWork (physics)Quality (philosophy)ConceptualizationHealth careEmergency nursingEmergency departmentNursingArgument (complex analysis)Prehospital Emergency CarePublic relationsSociologyMedical emergencyBusinessPsychologyMedicineEmergency medical servicesPolitical scienceComputer scienceLawEngineering

Abstract

fetched live from OpenAlex

Emergency care in large urban hospitals across the country is in the midst of major redesign intended to deliver quality care through improved access, decreased wait times, and maximum efficiency. The central argument in this paper is that the conceptualization of quality including the documentary facts and figures produced to substantiate quality emergency care is socially organized within a powerful ruling discourse that inserts the interests of politics and economics into nurses' work. The Canadian Triage and Acuity Scale figures prominently in the analysis as a high-level organizer of triage work and knowledge production that underpins the way those who administer the system define, measure and evaluate emergency care processes, and then use this information for restructuring. Managerial targets and thinking not only dominate the way emergency work is understood, determined, and controlled but also subsume the actual work of health-care providers in spaces called 'wait times', where it is systematically rendered 'unknowable'. The analysis is supported with evidence from an extensive institutional ethnography that shows what nurses actually do to manage the safe passage of patients through their emergency care process starting with the work of triage nurses.

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 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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.318
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations26
Published2013
Admission routes2
Has abstractyes

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