Relationship between nurses’ pain knowledge and pain management outcomes for their postoperative cardiac patients
Bibliographic record
Abstract
Nurses' knowledge and perceived barriers related to pain management have been examined extensively. Nurses have evaluated their pain knowledge and management practices positively despite continuing evidence of inadequate pain management for patients. However, the relationship between nurses' stated knowledge and their pain management practices with their assigned surgical cardiac patients has not been reported. Therefore, nurses (n=94) from four cardiovascular units in three university-affiliated hospitals were interviewed along with 225 of their assigned patients. Data from patients, collected on the third day following their initial, uncomplicated coronary artery bypass graft (CABG) surgery, were aggregated and linked with their assigned nurse to form 80 nurse-patient combinations. Nurses' knowledge scores were not significantly related to their patients' pain ratings or analgesia administered. Critical deficits in knowledge and misbeliefs about pain management were evident for all nurses. Patients reported moderate to severe pain but received only 47% of their prescribed analgesia. Patients' perceptions of their nurses as resources with their pain were not positive. Nurses' knowledge items explained 7% of variance in analgesia administered. Hospital sites varied significantly in analgesic practices and pain education for nurses. In summary, nurses' stated pain knowledge was not associated with their assigned patients' pain ratings or the amount of analgesia they received.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".