Support for a non-therapist assisted, Internet-based cognitive-behavioral therapy (iCBT) intervention for mental health in rheumatoid arthritis patients
Bibliographic record
Abstract
Anxiety is common in patients with rheumatoid arthritis (RA) and associated with worse RA outcomes. This study assessed the feasibility and preliminary health impacts (mental and physical) of a non-therapist assisted, online mental health intervention targeting anxiety in this population. Participants with confirmed RA and elevated anxiety symptoms were enrolled into the Worry and Sadness program, an Internet-based cognitive-behavioral therapy (iCBT) intervention for anxiety and depression shown to be effective in the general population. Validated self-report measures of anxiety, depression, pain interference, fatigue, physical health-related quality of life, functional status, and patient-reported disease severity were collected at baseline, post-intervention, and at three-month follow-up. Emotional distress scores were tracked between lessons. Participants provided qualitative feedback in writing post-intervention. We analyzed the responses of 34 participants; the majority was female (86%) and the mean age was 57 (SD = 13). Of these, 80% (n = 28) completed the study in its entirety. Among these completers, 94.1% described the program as worthwhile. We found statistically significant improvements in anxiety, depression and fatigue from baseline to three-month follow-up, with small to large effect sizes (d = 0.39–0.81). Post-hoc analyses revealed that statistically significant change occurred between baseline and post-intervention for anxiety and depression and was maintained at three-month follow-up, whereas statistically significant change occurred between baseline and three-month follow-up for fatigue. Statistically significant reductions in emotional distress occurred across the program, with a large effect size (d = 1.16) between the first and last lesson. The Worry and Sadness program shows promise as a feasible resource for improving mental health in RA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".