Factors influencing orthopedic nurses’ pain management: A focused ethnography
Bibliographic record
Abstract
Aim: The aim of this study was to explore the factors influencing orthopedic surgery nurses’ decisions to administer pro re nata (PRN) opioid analgesia for postoperative pain.Background: Fast-track surgery programs reduce length of stay by identifying and addressing factors causing patients to remain in hospital, including pain (H. Kehlet, Lancet. 2013;381:9878(9878)). The management of acute pain is an important component of quality care for patients after total knee arthroplasty.Methods: The study used a qualitative design of focused ethnography. Ten nurses working on an orthopedic surgery unit at a large urban hospital in western Canada participated in semistructured interviews that used a patient vignette to examine factors that influenced participants’ pain management in the context of fast-track surgery. Interviews were transcribed and analyzed using thematic analysis and constant comparison.Findings: Nurses described a complex clinical environment where the interplay of several factors informed decisions to administer PRN opioid analgesia. The unit’s culture and physical space influenced nurses’ assessments of pain and their decisions whether to treat pain with PRN opioids. Each nurse’s self-concept affected pain management decisions because of perceived importance of pain control and perceived duty to provide analgesics. The subjectivity of pain added another layer of complexity as nurses responded to the patient’s expression of pain from within the milieu of the unit culture and their unique self-concept.Conclusion: Understanding the complexity of factors that influence nurses’ postoperative pain management provides clinical nurses and nursing leaders with directions for future education and research, guided by the goal of continued improvement in pain management in the challenging setting of fast-track surgeries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".