The Geriatric Pain Measure: Validity, Reliability and Factor Analysis
Bibliographic record
Abstract
BACKGROUND: Pain is a multidimensional experience that should be evaluated beyond an estimate of intensity. A multidimensional pain measure has not been developed for older persons undergoing comprehensive geriatric assessment. OBJECTIVE: To develop and evaluate validity and reliability of a multidimensional pain assessment instrument for older persons. RESEARCH DESIGN: A series of steps in instrument development and evaluation. SUBJECTS: A total of 176 subjects (mean age 84 +/- 6.0 years) in ambulatory geriatric clinics; 64% were women, and 73% had a history of chronic pain. MEASUREMENTS: Measurements included the Geriatric Pain Measure (GPM), the McGill Pain Questionnaire, Yesavage GDS, Katz ADLs, Lawton IADLs, Tinetti Gait and Balance, Folstein MMSE, and other demographic and clinical characteristics from interview and chart review. RESULTS: The GPM demonstrated a standardized alpha = 0.9445, homogeneity ratio =0.457, and average inter-item correlation =0.415. A subgroup of 50 subjects demonstrated concurrent validity of the GPM in comparison with the McGill Pain Questionnaire (Pearson's r correlation 0.6269 (P < .0000). Test-retest reliability was demonstrated in another subgroup of 50 subjects who repeated the GPM within 48 to 72 hours (Pearson's r = 0.9018; P < .0000). Factor analysis revealed five clusters of components: Pain Intensity, Disengagement, Pain with Ambulation, Pain with Strenuous Activities, and Pain with Other Activities. CONCLUSIONS: The GPM is a 24-item questionnaire that is easy to administer and has significant validity and reliability in older persons with multiple medical problems. The GPM may be a useful addition to the multidimensional geriatric assessment process.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".