An Exploration of Non-Urgent Emergency Department Use from the Patient and Health Care Professional Perspective
Bibliographic record
Abstract
BACKGROUND: Patients present at the emergency department (ED) with a wide range of complaints that range significantly in acuity and complexity. To date, there has been a lack of research exploring factors influential in patients’ decision-making processes with respect to attending the ED for non-urgent matters.\nAIM: The purpose of this study was to study non-urgent ED use from both the patient and health care provider perspectives.\nMETHOD: This study used multiple methods. First, an integrative review was conducted which explored adult patient-reported reasons for using the ED non-urgently. Second, using qualitative methods, health care professionals (HCPs) were recruited from two EDs in Southwestern Ontario to explore perceptions on (a) the role of the ED within the larger healthcare system; (b) how non-urgent visits in the ED are contextualized; and (c) reasons for non-urgent use of the ED by patients. Third, a cluster analysis was performed which identified and characterized unique subgroups of ED patients based on their levels of stress and coping ability; groups were compared with respect to sociodemographic variables, clinical health information, and health literacy.\nRESULTS: Seven themes emerged from the integrative review: need to be risk averse with respect to the health issue; knowledge and awareness of alternative sources of care; dissatisfaction with primary care provider; satisfaction with ED; ED accessibility and convenience resulting in low access burden; referred to the ED by others; and relationships between patients and health care providers. In the qualitative study, HCPs reported that the role of the ED was a multifaceted place to receive quality care and act as a safety net. However, HCPs differentiated non-urgent ED patients by their underlying medical versus psychosocial dispositions. Finally, among 171 ED patients, there were no significant differences in stress, coping, or health literacy levels with respect to triage acuity. The cluster analysis showed three distinct groups of patients with those in Cluster 1 having the greatest levels of stress and avoidant/dysfunctional coping, combined with the lowest health literacy.\nCONCLUSION: Future studies should explore further biopsychosocial characteristics driving decision-making as well as their relationship to patients’ high estimation of medical severity.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".