Chronic Pain Assessment: A Seven‐Factor Model
Bibliographic record
Abstract
BACKGROUND: There are many measures assessing related dimensions of the chronic pain experience (eg, pain severity, pain coping, depression, activity level), but the relationships among them have not been systematically established. OBJECTIVE: The present study set out to determine the core dimensions requiring assessment in individuals with chronic pain. METHODS: Individuals with chronic pain (n=126) completed the Beck Anxiety Inventory, Beck Depression Inventory, Beck Hopelessness Scale, Chronic Pain Coping Index, Multidimensional Pain Inventory, Pain Catastrophizing Scale, McGill Pain Questionnaire--Short Form, Pain Disability Index and the Tampa Scale of Kinesiophobia. RESULTS: Before an exploratory factor analysis (EFA) of the nine chronic pain measures, EFAs were conducted on each of the individual measures, and the derived factors (subscales) from each measure were submitted together for a single EFA. A seven-factor model best fit the data, representing the core factors of pain and disability, pain description, affective distress, support, positive coping strategies, negative coping strategies and activity. CONCLUSIONS: Seven meaningful dimensions of the pain experience were reliably and systematically extracted. Implications and future directions for this work are discussed.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".