Understanding the High Frequency Use of the Emergency Department for Patients With Chronic Pain: A Mixed-Methods Study
Bibliographic record
Abstract
INTRODUCTION: Chronic pain (CP) is a common driver of emergency department (ED) visits despite the ED not being the ideal setting for CP because of increased risk of adverse events and high costs. PURPOSE: The purpose of this study is to understand factors contributing to CP-related ED visits, patients' care experiences, and patients' perspectives on alternatives to the ED. METHODS: We used a mixed-methods design combining semi-structured interviews and questionnaires with 12 patients with CP who had 12 or more ED visits over 1 year. We analyzed test scores using descriptive statistics and interviews using applied thematic analysis. RESULTS: Four themes emerged. Factors contributing to ED visits included the following: fear (e.g., pain and its impact); inability to cope with pain; family suggestions to go to the ED; and access to other services and resources. Patients had validating and invalidating experiences in the ED: needs were met or not met; and feeling acknowledged or unacknowledged. Patients' experiences with their family physician included feeling supported or unsupported. Alternatives to the ED included working with an interdisciplinary team, developing personalized care plans, and increased community-based resources. CONCLUSIONS: Patients with CP and frequent ED use present with complex pain and care experiences, requiring careful attention to management strategies and the patient-provider relationship.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".