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Record W4412587433 · doi:10.1177/23743735251362529

Exploring Factors That Drive Nonurgent Emergency Department Use

2025· article· en· W4412587433 on OpenAlexaff
Carina Mireles-Romo, In-Ho Choi, Jennifer Roh, Saadat Soheil, Shannon Toohey

Bibliographic record

VenueJournal of Patient Experience · 2025
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsDouglas College
Fundersnot available
KeywordsEmergency departmentMedicineOvercrowdingTriageMedical emergencyDemographicsHealth careTrauma centerFamily medicinePrimary careEmergency medicineNursingRetrospective cohort study

Abstract

fetched live from OpenAlex

Nonurgent emergency department (ED) visits continue to rise despite efforts to reduce ED overcrowding. This study aimed to explore factors influencing ED utilization and perceptions of alternative healthcare services among patients of nonurgent ED visits. Conducted at an academic, level-1 trauma center, the study identified nonurgent visits using the emergency severity index 5-level triage acuity scale and utilized a 3-part qualitative survey to gather data on demographics, reasons for ED visits, and perceptions of the ED, primary care physicians (PCPs), and urgent care centers (UCCs). Survey responses from 586 patients were analyzed, demonstrating common themes such as ED accessibility, physician qualifications, and the need for diagnostic testing. Although participants generally expressed satisfaction with PCPs and UCCs, the frequency of nonurgent ED visits remained high. The study suggests that many nonurgent cases could have been managed using alternative healthcare services; additionally, the findings align with existing literature and support the need for enhanced patient education on appropriate ED use and the benefits of utilizing alternative healthcare options.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.470

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 head0.116
GPT teacher head0.333
Teacher spread0.217 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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