Confirmatory factor analysis of 2 versions of the Brief Pain Inventory in an ambulatory population indicates that sleep interference should be interpreted separately
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it