More than my RA: A randomized trial investigating body image improvement among women with rheumatoid arthritis using a functionality-focused intervention program.
Bibliographic record
Abstract
OBJECTIVE: Negative body image is prevalent in women with rheumatoid arthritis and can affect other areas of well-being. Patients have expressed desire for body image to be addressed in treatment. Yet, it is not routinely addressed and no experimental intervention research has been conducted, until now. This randomized trial evaluated a brief online body image intervention for women with rheumatoid arthritis, with a focus on body functionality (everything the body is capable of doing) as the primary technique. METHOD: = 44.82) were randomized to the Expand Your Horizon intervention (comprising 3 writing exercises focusing on body functionality) or a waitlist control group. Primary outcomes concerned body image and secondary outcomes related to rheumatoid arthritis; these outcomes were assessed at pretest, posttest, and at 1-week and 1-month follow-up. RESULTS: Multilevel modeling analyses showed that, relative to control, participants in the intervention experienced improvements in various aspects of body image (functionality appreciation, body appreciation, body satisfaction, body-self alienation) and decreases in depression, with effects persisting at 1-week and 1-month follow-up. No intervention effects were found for body-self harmony, rheumatoid arthritis-specific disability, pain-related disability, and anxiety. CONCLUSIONS: Focusing on body functionality can improve body image and reduce depression in women with rheumatoid arthritis. The intervention technique is easy and affordable to deliver and could be a fruitful addition to extant treatments for rheumatoid arthritis. (PsycINFO Database Record
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".