Anxiety in rheumatoid arthritis (RA): Support for a biopsychosocial approach to the management of RA
Bibliographic record
Abstract
Anxiety is highly prevalent among patients with rheumatoid arthritis (RA). Although this co-occurrence is associated with a range of adverse functional, mental, and physical health outcomes (e.g., more joint abnormalities, greater disease activity, higher functional disability, more severe pain and fatigue), the management of RA remains reliant on a biomedical approach. Guided by a biopsychosocial framework, this dissertation aimed to both clarify and add to the extant literature on comorbid anxiety in RA. Specifically, (1) I examined the possibility of distinct anxiety trajectory groups within an RA sample as well as unique correlates associated with group membership; and (2) I tested the feasibility and potential benefit of an accessible anxiety intervention among RA patients. Using a clinical sample of individuals with RA (N=154), Study 1 identified three distinct anxiety trajectory groups uniquely associated with a number of clinical indicators cross-sectionally and longitudinally. More severe anxiety was associated with worse outcomes (i.e., functional disability, tender joint count, pain, and fatigue) and persistent moderate anxiety was associated with worsened fatigue over time. Study 2 supported the feasibility of an Internet-based cognitive-behavioral therapy (iCBT) intervention for anxiety (and depression) in RA patients (N=34) through recruitment, adherence, and qualitative patient feedback. Preliminary evidence of mental (i.e., anxiety, depression, and emotional distress) and physical (i.e., fatigue) improvements following participation in the intervention was also provided. Results of this dissertation hold important implications for the assessment and treatment of anxiety in the context of RA. Findings are discussed in relation to a stepped care model and possible amendments to current Canadian healthcare practices are reviewed.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".