“They Just Don’t Want to Feel Forgotten”: A Mixed-Methods Research on Patient Satisfaction With Wait Times in Emergency Departments
Bibliographic record
Abstract
Patient satisfaction, an essential care quality measure, is heavily impacted by wait times. This study examined how different factors affect patient satisfaction with ED wait times in Newfoundland and Labrador, Canada. This mixed-method study, conducted in 4 EDs, used data collected using telephone surveys and semistructured interviews. Patient satisfaction with (1) physician initial assessment and (2) length of stay were analyzed using ordinal regression and thematic analysis. Among the 766 participants, 12% were extremely dissatisfied with physician initial assessment, and 13% were extremely dissatisfied with length of stay. Patients well-informed about the delays were more likely to report higher satisfaction than those who were not informed (aOR = 2.43, 95% CI [1.48-3.99], P -value <.001). Qualitative analysis revealed 4 key themes: poor communication about wait times, lengthy wait times, resource shortages causing long ED wait times, and patients avoiding ED because of it. Our study shows that patients are better satisfied when they are well-informed about the delays. This helped patients feel “less forgotten.” Addressing wait time issues is critical to providing patient-centered care and optimizing the care experience.
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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 | 0.056 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".