The Utility and Construct Validity of Four Measures of Pain Intensity: Results from a University-Based Study in Spain
Bibliographic record
Abstract
OBJECTIVE: Pain intensity is the most commonly assessed domain in pain research and clinical settings. To facilitate cross-cultural research, knowledge regarding the psychometric properties of pain intensity measures in individuals from different countries is needed. However, the majority of this research has been conducted in English-speaking countries. DESIGN: Survey study. SETTING: University. SUBJECTS: Four hundred nineteen college students. METHODS: Participants were asked to complete four measures assessing average pain intensity: 1) the 0-10 numerical rating scale (NRS-11), 2) the 100-mm visual analog scale (VAS), 3) the four-point verbal rating scale (VRS-4), and 4) the Faces Pain Scale-Revised (FPS-R). RESULTS: The rates of incorrect completion of the four scales were uniformly low (range = 1-2%). The NRS-11 had the highest preference rate (31%), although a substantial number of participants also preferred each of the other three scales (range = 22-24%). The findings support the utility and construct validity of all four pain intensity scales in this Spanish-speaking sample. CONCLUSIONS: When considered in light of research from other non-English-speaking samples indicating significant psychometric weaknesses for the NRS-11 and VAS and relative strengths of the FPS-R in some groups, the findings suggest that the FPS-R might be the most appropriate pain intensity scale to use when comparisons across populations from different countries is a goal. More research is needed to determine the extent to which demographic (i.e., age, education levels, socioeconomic status) vs cultural factors (i.e., country of origin) influence the reliability, validity, and utility of different pain measures.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".