Utilization of the Emergency Department and Predicting Factors Associated With Its Use at the Saudi Ministry of Health General Hospitals
Bibliographic record
Abstract
Overuse of emergency rooms (ER) is a public health problem. To investigate this issue, a cross-sectional survey was conducted at the ERs of King Abdul-Aziz Hospital, King Fahd Hospital, and Al-Thaghor Hospital in November 2013 with the aims of estimating emergency service utilization for non-urgent cases, identifying the predictors of ER utilization for non-urgent cases, and measuring patients' knowledge of primary healthcare centers (PHCCs). Patients were interviewed using a structured questionnaire and the data were analyzed using the Statistical Package for the Social Sciences. We recruited 300 patients; males comprised 50.7% of the sample. A higher proportion of patients with non-urgent cases visited the ER three to four times a year (P=0.001). A higher proportion of patients without emergencies had not attempted to visit an outpatient clinic before the ER (P=0.003). Most patients without emergencies thought the ER was the first place to consult in case of illness. Most patients who visited the ER were single, <15 years, and had lower incomes. Patients requested ER services for primary care-treatable conditions because of limited services and resources as well as limited working hours at PHCCs. Most patients (90.0%) were knowledgeable about PHCCs, with those of lower education being more knowledgeable. Patients reported long ER waiting times (≥3 hours), no organization (85.9%), and lack of medical staff. Overall, overuse of ER services is high at the Ministry of Health hospitals in Jeddah. The risk factors for ER overuse are age<15 years, singlehood, and low incomes. Policy makers and health providers have a challenging task to control ER overuse. We recommend developing strategies to implement policies aimed at reducing non-urgent ER use as well as making healthcare services more available to the population.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".