Bibliographic record
Abstract
Relieving patients' pain following surgery is a major nursing goal. However, patients report that effective postoperative pain management is often not achieved (Soderhamn & Ivall, 2003). Effective pain management may be facilitated when nurses use empathic responses with patients. To date, the effectiveness of empathic responses has not been well-grounded in research evidence. The purpose of this study is to examine the relationship between nurses' empathic responses, analgesic administration and patients' reports of pain intensity following orthopedic surgery. The conceptual framework is the Gate Control Theory of Pain (Melzack & Wall, 1965). The setting is do two moderate size hospitals in the Mid-Western United States. A convenience sample of 60 nurses and 120 patients who have had a total hip replacement will be recruited to participate. Sixty nurses will complete the Staff-Patient Interaction Response Scale (SPIRS) (Gallop, Lancee, & Garfinkel, 1989) and the Toronto Pain Management Inventory (TMPI) (Watt-Watson, 1987). Patient instruments will complete the McGill Pain Questionnaire-Short Form (MPQ-SF) (Melzack, 1987) and the Nurse Attends to Pain Scale (NAPS) (Watt-Watson, 2000). Results of the study will clarify the usefulness of empathic responses as a nursing strategy to manage postoperative 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.126 |
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".