Patient Experience of Emergency Department Care in Newfoundland and Labrador: A Structural Equation Modeling Analysis
Bibliographic record
Abstract
Context: Over the last decade, patient care experience in the emergency department (ED) has been subpar. Relationships and communication with staff, physical comfort, privacy, and accessibility influence the patient experience of ED care. Understanding factors that impact the ED care experience is needed to improve the quality of care and meet patient needs and expectations. Objective: To examine the factors that impact patient experience of ED care in Newfoundland and Labrador (NL) and how patient ED care experience differs by gender, age and ED location. Study Design: Cross-sectional study. Analysis: Structural Equation Modelling (SEM) Analysis. A p-value of <0.05 was considered statistically significant. Setting: Two rural and two urban EDs in NL. Population Studied: Patients visiting the EDs from 1 March 2021 to 27 July 2023 randomly selected based on their visit date and time. Instrument: Telephone survey administered by a trained interviewer. Outcome Measures: Overall patient experience of care, patient experience with aspects of care delivery such as staff concern for their comfort, support received for fears/worries, clarity of explanations, responsiveness to requests, and involvement in care decisions. Results: All the outcome variables were correlated (r = 0.04–0.4, P<0.001), and thus, we used a latent variable for the SEM analysis. The model fit the observed data well [CFI=0.96, SRMR=0.05, RMSEA = 0.196]. All the variables had a factor loading of ≥0.7 with the latent variable, except patient experience with staff responsiveness to requests. The final analysis showed that age, gender and hospital location are associated with patient ED care experience. Older patients reported a better experience than younger patients [OR 1.11, CI 1.04-1.19]. Male patients reported a better experience than female patients [OR 1.15, CI 1.06-1.25], and patients who visited the urban EDs reported a worse experience than those who visited the rural EDs [OR 0.76, CI 0.70-0.82]. Conclusions: Our findings will offer a comprehensive examination of the factors influencing the quality of patient care experiences within EDs and how patient demographics and the geographical location of the ED affect it. As these insights are essential for enhancing patient-centred care and ultimately assisting in improving health outcomes, our results are a necessary resource for NL health services.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".