The Impact of Nurses' Empathic Responses on Patients' Pain Management in Acute Care
Bibliographic record
Abstract
BACKGROUND: Although nurses have the major responsibility for pain management, little is known about nurses' responses to patients in the process of managing acute pain. OBJECTIVE: To examine the relationship between nurses' empathic responses and their patients' pain intensity and analgesic administration after surgery. METHODS: Two hundred twenty-five patients from four cardiovascular units in three university-affiliated hospitals were interviewed on the third day after their initial, uncomplicated coronary artery bypass graft (CABG) surgery about their pain and current pain management. Concurrently, their nurses' (n = 94) empathy and pain knowledge and beliefs were assessed. Patient data were aggregated and linked with the assigned nurse to form 80 nurse-patient pairs. RESULTS: Nurses were moderately empathic, and their responses did not significantly influence their patients' pain intensity or analgesia administered. Patients reported moderate to severe pain but received only 47% of their prescribed analgesia. Patients' perceptions of their nurse's attention to their pain were not positive, and empathy explained only 3% of variance in patients' pain intensity. Deficits in knowledge and misbeliefs about pain management were evident for nurses independent of empathy, and knowledge explained 7% of variance in analgesia administered. Hospital sites varied significantly in analgesic practices and pain inservice education for nurses. CONCLUSIONS: Empathy was not associated with patients' pain intensity or analgesic administration.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".