Factors associated with non-urgent visits to the emergency department in a tertiary care centre, western Saudi Arabia: cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: To explore magnitude and factors associated with non-urgent visits to the emergency department (ED) in a tertiary care centre, western Saudi Arabia. DESIGN: A cross-sectional study. SETTING: ED of a tertiary care hospital in western Saudi Arabia. PARTICIPANTS: 400 patients, both men and women. INTERVENTIONS: An interview-based questionnaire was administered to a consecutive sample of patients who visited the ED during morning shifts, where primary healthcare centres (PHCs) and outpatient clinics were available. PRIMARY OUTCOME MEASURE: ED visits classified as non-urgent versus urgent (excluding life-threatening conditions) based on the Canadian Triage and Acuity Scale (CTAS). Levels IV and V were classified as non-urgent. RESULTS: Majority of the cases were adult (97.3%) and half of them were women (54.8%). Non-urgent visits represented 78.5% among non-life-threatening cases. One-third of the patients (33.8%) had three visits or more to the ED during the past year. Main reasons for visiting the ED were perception that the condition was urgent (42.0%), easy access (25.5%) and limited resources and services at the PHCs (17.8%). Patients 40-50 years old were more likely to have non-urgent visits (OR=3.21, 95% CI 1.15 to 8.98). However, likelihood of non-urgent visits was significantly lower among patients with cancer (OR=0.37, 95% CI 0.19 to 0.72) and cardiovascular disease (OR=0.43, 95% CI 0.23 to 0.83), and those who live near the hospital (OR=0.49, 95% CI 0.28 to 0.88). CONCLUSIONS: The current study reported overuse of the ED. Enhancement of the primary care services, in concordance with community awareness, is an important component to reduce burden due to non-urgent use of the ED.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".