Psychometric Properties of Pain Scales in Inpatient Settings: An Umbrella Review
Bibliographic record
Abstract
AIMS: To identify the pain assessment scales with the best psychometric properties to be used by nurses in an inpatient setting. DESIGN: Umbrella review. METHODS: A comprehensive search of four databases was conducted for systematic reviews published from July 2013 to November 2024, focusing on psychometric properties of pain scales used in inpatient settings. Inclusion criteria required scales to assess subjective or behavioural pain and be nurse-administered, while reviews without detailed psychometric data were excluded. Screening, quality appraisal (JBI checklist), and data extraction were performed independently by two researchers. Data synthesis combined qualitative and quantitative approaches, with psychometric properties evaluated using the COSMIN checklist. The study was reported in accordance with the Preferred Reporting Items for Overviews of Reviews (PRIOR) statement. RESULTS: Seventeen articles met the inclusion criteria, identifying 41 scales used across various patient populations, including critical care, paediatric, postoperative, cancer, cerebral palsy, disorders of consciousness, low back and neck pain, stroke and verbal communication disorders. The Paediatric Pain Profile, the Breakthrough Pain Assessment Tool and the Questionnaire on Pain caused by Spasticity demonstrated adequate psychometric properties, although the positive findings for the latter two should be confirmed by at least one additional study. Most of the scales (n = 36) require further studies to validate their use in clinical practice. For two scales, their clinical use remains questionable. CONCLUSION: The Paediatric Pain Profile, the Breakthrough Pain Assessment Tool, and the Questionnaire on Pain caused by Spasticity can be recommended for use. Unidimensional scales should complement, rather than replace, multidimensional scales to ensure a comprehensive pain assessment. Standardising documentation with validated scales enhances clinical decision-making, care quality, research usability, and reduces documentation burden.
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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 | 0.071 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.029 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".