Improving the patient's experience in the emergency department during COVID-19 pandemic: a community-based analysis from Western Saudi Arabia.
Bibliographic record
Abstract
Background Patient satisfaction is an important measure of the health care encounter. It is challenging to achieve a perfect patient experience during the current COVID-19 pandemic, especially from an emergency department visit. Aim This study aimed to assess the factors that improve patient experience during an emergency department (ED) visit in the western region of Saudi Arabia. Methods This is a cross-sectional study, conducted over a month from January to February 2021. Via an electronic survey tool, we used the de en (EQS-H) to measure patients’ satisfaction with their ED encounter. Results The total level of satisfaction was high in 43.66% (n=224) of participants, moderate in 37.04% (n=190), and 19.29% (n=99) were unsatisfied. We noted significant predictors of dissatisfaction, including increasing age, higher educational level, and the existence of chronic diseases. A clear treatment plan and discharge instructions were important determinants for improving patient satisfaction. Conclusion The determinants of patient satisfaction during an ED visit are an important quality marker of the emergency department encounter. Such findings should be used as a benchmark for future programs aiming to improve patients’ experience during ED visits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".