Emergency department patients’ self-perceived medical severity and urgency of care: The role of health literacy, stress and coping
Bibliographic record
Abstract
OBJECTIVES: This study aimed to (1) compare the agreement between triage acuity and emergency department (ED) patients' self-perceived medical severity and urgency, and (2) examine how health literacy, stress, and coping relate to patients' perceptions of medical need and urgency. METHODS: In this cross-sectional, observational study, 171 patients from a large acute care teaching hospital in Southwestern Ontario were recruited in autumn 2020. English-speaking adults (18 + years) with Canadian Triage Acuity Scale (CTAS) scores from 2 (emergent) to 5 (non-urgent) were included. Patients completed surveys on stress (Perceived Stress Scale), coping (Brief Coping with Problems Experienced), and health literacy (Health Literacy Questionnaire). Electronic medical records linked ED utilization data with patient-reported data. Agreement between CTAS and patients' self-assessed severity and urgency was analyzed using crosstabs and Cohen's kappa. RESULTS: A total of 171 patients were recruited. There were no significant differences between ED patients with varying triage acuities and stress, coping, or health literacy levels. Cohen's kappa statistics showed poor agreement between triage nurse-assigned scores and patients' self-perceived medical severity and urgency of care. Those who overestimated were younger, single, had low medical acuity (CTAS 4/5), and lower understanding of how to navigate the health care system. Conversely, those who underestimated were older, married, and had high medical acuity (CTAS 2). CONCLUSIONS: Future studies should focus on exploring the underlying factors (e.g., sociodemographic variables, clinical health information, and other personal attributes) contributing to discrepancies between patient-perceived severity and triage assessments in a larger, more representative sample.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".