Pain coping skills training for African Americans with osteoarthritis: results of a randomized controlled trial
Bibliographic record
Abstract
African Americans bear a disproportionate burden of osteoarthritis (OA), but they have been underrepresented in trials of behavioral interventions for pain. This trial examined a culturally tailored pain coping skills training (CST) program, compared to a wait list control group, among 248 African Americans with knee or hip OA. The pain CST program involved 11 telephone-based sessions over 3 months. Outcomes were assessed at baseline, 3 months (primary), and 9 months, and included the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale (primary outcome), WOMAC total score and function subscale, PROMIS Pain Interference, Short-Form 12 Mental and Physical Composite Subscales, Coping Strategies Questionnaire-Total Coping Attempts, Pain Catastrophizing Scale, Patient Health Questionnaire-8, Arthritis Self-Efficacy Scale, and Patient Global Impression of Arthritis Symptom Change. Linear mixed models were fit for all outcomes. There were no significant between-group differences in WOMAC pain score at 3 months (-0.63 [95% confidence interval -1.45, 0.18]; P = 0.128) or 9 months (-0.84 [95% confidence interval -1.73, 0.06]; P = 0.068). Among secondary outcomes, at 3 months, there were significant differences, in favor of the CST group, for Coping Strategies Questionnaire Total Coping Attempts, Pain Catastrophizing Scale, Arthritis Self-Efficacy, and Patient Global Impression of Arthritis Symptom Change (P < 0.01). Coping Strategies Questionnaire Total Coping Attempts, Arthritis Self-Efficacy, and Patient Global Assessment Change were also significantly improved at 9 months in the CST group, compared with wait list (P < 0.01). The culturally tailored pain CST program did not significantly reduce pain severity but did improve key measures of pain coping and perceived ability to manage pain among African Americans with OA.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".