Patient Satisfaction and Documentation of Pain Assessments and Management After Implementing the Adult Nonverbal Pain Scale
Bibliographic record
Abstract
BACKGROUND: Accurate assessment and management of pain in critically ill patients who are nonverbal or cognitively impaired is challenging. No widely accepted assessment tool is currently in place for assessing pain in these patients. OBJECTIVES: To evaluate the effect of implementing a new pain assessment tool in a trauma/neurosurgery intensive care unit. METHODS: Staff and patient satisfaction questionnaires and retrospective chart reviews were used before and after implementation of the Nonverbal Pain Scale. The questionnaire responses, frequency of pain documentation, and amount of pain medication given were compared from before to after implementation. RESULTS: Most staff (78%) ranked the tool as easy to use. Implementation of the tool increased staff confidence in assessing pain in nonverbal, sedated patients (57% before vs 81% after implementation, P = .02) and increased the number of pain assessments documented by the nursing staff for noncommunicative patients per day in the intensive care unit (2.2 before vs 3.4 after, P = .02). Patients reported decreased retrospective pain ratings (8.5 before vs 7.2 after, P = .04) and a trend toward a decrease in the time required to receive pain medication (38% before vs 10% after requiring >5 minutes to receive medication, P = .06). CONCLUSIONS: Implementation of the Nonverbal Pain Scale in a critical care setting improved patients' ratings of their pain experience, improved documentation by nurses, and increased nurses' confidence in assessing pain in nonverbal patients.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".