Nurses’ assessment of pain in surgical patients
Bibliographic record
Abstract
AIM: This paper reports a study to compare nurses' ratings of pain intensity and suffering (affect) in adult surgical patients with patients' own ratings of these variables, and to investigate whether pain ratings were influenced by cultural and ethnic differences. BACKGROUND: Studies show that postoperative pain continues to be under-treated in a large proportion of cases. The problem may be partly due to inaccurate pain assessment by nurses. METHOD: A convenience sample of 95 patients and 95 nurses in adult surgical units was selected from four hospitals in Jerusalem, Israel in 2003-2004. A questionnaire was administered to each patient by the researcher. The questionnaire included: (a) a Hebrew translation of the Short-Form McGill Pain Questionnaire for pain sensation, pain affect, and present pain intensity at rest and on movement; (b) visual analogue scales for overall pain intensity, suffering, and satisfaction with treatment; and (c) demographic and cultural data. Within a few minutes of the patient completing the questionnaire, a nurse who had been allocated to care for that patient made an independent assessment of the patient's pain. The nurse then left the patient's room and filled in the same questionnaire. FINDINGS: Nurses significantly underestimated all dimensions of pain on the above scales, but accurately assessed patient treatment satisfaction. There were no statistically significant effects for cultural and ethnic differences in pain assessment. Both types of clinical area where nurses worked and nurses' level of nursing education were found not to influence their assessment. CONCLUSION: The findings have implications for the management of postoperative pain by highlighting the need for more accurate pain assessment. Further research is required to elucidate the way in which nurses and patients conceptualize pain and to understand better the process of pain assessment in clinical nursing practice.
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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 | 0.008 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".