A Regional Survey to Determine Factors Influencing Patient Choices in Selecting a Particular Emergency Department for Care
Bibliographic record
Abstract
OBJECTIVES: Increases in regional emergency department (ED) efficiencies might be obtained by shifting patients to less crowded EDs. The authors sought to determine factors associated with a patient's decision to choose a specific regional ED. Based on prior focus group discussions with volunteers, the hypothesis was that distance to a specific ED and perceived ED wait times would be important. METHODS: A cross-sectional survey was developed using qualitative focus group methodology. The resulting survey was composed of 17 questions relating to patient decisions in choosing a specific ED and was administered in each of six EDs in a single urban Canadian health region at all hours of the day. Ambulatory patients with a Canadian Triage and Acuity Scale (CTAS) level 3 to 5 and aged ≥19 years were surveyed. The primary outcome was the proportion of patients whose main motivation for attending a specific ED was either distance traveled to reach the ED or perceived ED waiting time. Multivariable logistic regression was performed to assess factors influencing both of these reasons. RESULTS: A total of 757 patients were approached and 634 surveys (83.8%) were completed. Distance from the ED (named by 44.0% of respondents as their primary reason) and perceived ED wait times (9.3%) were the main motivations for patients to attend a specific ED. Multivariable analysis of factors associated with choosing distance revealed that ED distance < 10 km (adjusted odds ratio [OR] = 2.20, 95% confidence interval [CI] = 1.45 to 3.33; p = 0.001) and age ≥ 60 years (adjusted OR = 1.58, 95% CI = 1.12 to 2.26; p = 0.04) were significant in choosing a particular ED. Multivariable analysis of factors influencing wait times demonstrated that having a painful complaint (adjusted OR = 1.42, 95% CI = 1.05 to 1.98; p = 0.047) and age < 60 years (OR = 1.47, 95% CI = 1.02 to 2.14; p = 0.049) were significant in choosing a particular ED. CONCLUSIONS: In a multicenter survey of patients from an urban health region, distance to a specific ED and perceived ED wait times were the most important reasons for choosing that ED. Younger patients and those with painful conditions appear to place greater priority on wait times.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".