The psychometric assessment of the older adult in pain: A systematic review of assessment instruments
Bibliographic record
Abstract
We conducted a systematic review of pain assessment tools suitable for community-dwelling older adults. For this work, we conceptualized existing psychometric tools as falling under the following domains: a) pain intensity/characteristics; b) pain-related interference/disability; c) coping strategies; d) pain beliefs/attitudes/cognitions; e) pain-related fear and anxiety; and f) pain-specific emotional distress. Multi-dimensional and condition-specific tools were also considered. The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology for systematic reviews of patient-reported outcome measures guided the evaluation of measurement properties, quality of evidence ratings, and recommendations for each measure. A search of Medline, PsycINFO, Web of Science, and the Cumulative Index of Nursing and Allied Health Literature, yielded a total of 21,755 records. Of these, 120 studies, focusing on 57 psychometric tools, were included in this review and categorized into the aforementioned pain assessment domains. The availability of psychometric studies with older adult populations was insufficient for most tools and the quality of evidence ranged from very low to high. Only a small number of tools met the criteria for a strong or tentative recommendation favoring their use. We identified gaps that should be addressed in future research. • Pain assessment tools were classified into domains (e.g., intensity, coping). • Quality of evidence and measurement properties vary across assessment domains. • Only few tools have undergone adequate psychometric evaluation with older adults. • Tools assessing pain intensity had the strongest psychometric support. • More research is needed in this area.
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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 | 0.047 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".