The Effect of a Nurse Initiated Therapeutic Conversation Compared to Standard Care for Patients With Acute Pain in the ED
Bibliographic record
Abstract
Acute pain is a common presenting complaint in the emergency department (ED) and is most often treated with opioid or nonopioid analgesia. However, studies have shown that receiving analgesia alone does not always influence patient satisfaction with pain management in the ED. Pain anxiety and catastrophizing have been shown to affect pain intensity and patients' response to analgesia. The objective of this study was to determine whether a brief therapeutic conversation would improve patient satisfaction with pain management compared with standard care for adult patients presenting to the ED with moderate to severe acute pain. Adult (18 years or older) patients presenting to the ED with moderate to severe acute pain were randomized to either the standard care group or the intervention group. Patients in the intervention group participated in a brief therapeutic conversation with an ED nurse to discuss their perceived cause of pain, level of anxiety, and expectations of their pain management. Prior to discharge, all patients were asked to complete a self-reported, 9-item questionnaire to assess their level of satisfaction with their overall ED experience. A total of 166 patients (83 in each group) were enrolled. Patient satisfaction with ED pain management and the proportion of patients who received analgesia in the ED were similar in both the control (n = 57; 68.7%) and intervention (n = 58; 69.9%) groups (Δ 1.2%; 95% CI [12.6, 15]). Qualitative findings demonstrate that patients place high importance on acknowledgment from ED staff and worry about the unknown cause of pain. This study suggests that patient satisfaction with pain management in the ED is multifactorial and complex. Further research should investigate additional methods of integrating nurse-led interventions into the care of patients in acute pain.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".