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Record W3172498269 · doi:10.52609/jmlph.v1i1.9

Improving the patient's experience in the emergency department during COVID-19 pandemic: a community-based analysis from Western Saudi Arabia.

2021· article· en· W3172498269 on OpenAlexvenueno aff
Khadijah Banjar, Sharafaldeen Bin Nafisah

Bibliographic record

VenueThe Journal of Medicine Law & Public Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency departmentPandemicPatient satisfactionMedicineCoronavirus disease 2019 (COVID-19)Patient experienceHealth careMedical emergencyFamily medicineNursingDisease

Abstract

fetched live from OpenAlex

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.

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)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.240
GPT teacher head0.475
Teacher spread0.235 · 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.

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

Citations6
Published2021
Admission routes1
Has abstractyes

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