A Mixed-Methods Analysis of Negative Patient Experiences in Emergency Department Care: Identifying Challenges and Evidence-Informed Strategies Across the Care Continuum
Bibliographic record
Abstract
This mixed-methods study explores negative patient experiences within emergency departments (EDs), aiming to uncover systemic challenges and propose evidence-informed solutions. Of 2114 shared ED experiences, 306 (14.5%) were reported as "bad" or "very bad." Younger age, Indigenous status, financial instability, mental health disabilities, and non-heteronormative sexual identities were associated with negative ED experiences. Our research highlights key issues across the ED care continuum. During triage and registration, patients felt judged and perceived that their health concerns were under prioritized. Prolonged wait times contributed to feelings of neglect. During assessments, privacy concerns and lack of communication were prominent. Perceptions of misdiagnosis and stigmatization emerged as major concerns during the diagnosis and treatment phases. At discharge, insufficient follow-up and unclear instructions were frequently reported. Our findings underscore the need for improved communication, enhanced training to reduce stigma, and multi-pronged strategies to address the root causes of patient dissatisfaction. These insights can guide healthcare practitioners and policymakers in fostering a more inclusive and supportive ED environment, ultimately improving patient experiences and outcomes.
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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 | 0.049 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".