Improving the Usefulness of the Multidimensional Pain Inventory
Bibliographic record
Abstract
BACKGROUND: The Multidimensional Pain Inventory (MPI) is a reliable and valid self-report instrument that measures the impact of pain on an individual's life, quality of social support and general activity. Criticism of the MPI has focused on this instrument's internal structure and the stability of its classification taxonomy. OBJECTIVES: To determine whether empirical summary scales could be developed for the MPI based on a large sample of respondents diagnosed with fibromyalgia syndrome. It was hypothesized that summary scales would improve the psychometric quality of the MPI and increase the stability of respondents' taxonomy profiles across time. METHODS: Respondents completed the MPI on two occasions before their admission to a multidisciplinary pain management program. RESULTS AND CONCLUSIONS: Based on principal components analysis, three summary scales were developed that reflected level of impairment, social support and activity. Summary scales possessed good psychometric qualities and, when cluster analyzed, replicated the MPI taxonomy. Exploratory analyses of the MPI taxonomy revealed that goodness-of-fit values generally became less reliable as respondent profiles approached the overall sample mean. When the relative distance between respondents fit to taxonomy profiles and the distance from the sample mean was considered, profile stability using summary scales was predicted with good precision. These results suggest that summary scales may enhance the usefulness of the MPI, and that the traditional method of determining profile fit within the MPI is not stable and needs to be reconsidered.
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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 | 0.021 | 0.078 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".