Confirmatory factor analysis of 2 versions of the Brief Pain Inventory in an ambulatory population indicates that sleep interference should be interpreted separately
Bibliographic record
Abstract
BACKGROUND: The Brief Pain Inventory (BPI-SF) is a widely-used generic pain interference scale, however its factor structure remains unclear. An expanded 10-item version of the Interference subscale has been proposed, but the additional value of the 3 extra items has not been rigorously evaluated. The purpose of this study was to evaluate and contrast the factorial and concurrent validity of the original 7-item and 10-item versions of the BPI-SF in a large heterogeneous sample of patients with chronic pain. METHODS: Exploratory and confirmatory factor analyses were conducted on independent subsets of the sample, and concurrent correlations with scales capturing similar constructs were evaluated. RESULTS: Two independent exploratory factor analyses (n=500 each) supported a single interference factor in both the 7- and 10-item versions, while confirmatory factor analysis (N=1000) suggested that a 2-factor structure (Physical and Affective) provided better fit. A 3-factor model, where sleep interference was the third factor, improved in model fit further. There was no significant difference in model fit between the 7- and 10-item versions. Concurrent associations with measures of general health, pain intensity and pain-related cognitions were all in the anticipated direction and magnitude and were not different by version of the BPI-SF. CONCLUSIONS AND IMPLICATIONS: The addition of 3 extra items to the original 7-item Interference subscale of the BPI-SF did not improve psychometric properties. The combined results lead us to endorse a 3-factor structure (Physical, Affective, and Sleep Interference) as the more statistically and conceptually sound option.
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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 | 0.054 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".