Cumulative Disadvantage and Disparities in Depression and Pain Among Veterans With Osteoarthritis: The Role of Perceived Discrimination
Bibliographic record
Abstract
OBJECTIVE: Perceived discrimination is associated with chronic pain and depression and contributes to racial health disparities. In a cohort of older adult veterans with osteoarthritis (OA), our objective was to examine how membership in multiple socially disadvantaged groups (cumulative disadvantage) was associated with perceived discrimination, pain, and depression. We also tested whether perceived discrimination mediated the association of cumulative disadvantage with depression and pain. METHODS: We analyzed baseline data from 270 African American veterans and 247 White veterans enrolled in a randomized controlled trial testing a psychological intervention for chronic pain at 2 Department of Veterans Affairs medical centers. Participants were age ≥50 years and self-reported symptomatic knee OA. Measures included the Everyday Discrimination Scale, the Patient Health Questionnaire Depression Scale, the Western Ontario and McMaster Universities Osteoarthritis Index pain subscale, and demographic variables. Cumulative disadvantage was defined as the number of socially disadvantaged groups to which each participant belonged (i.e., self-reported female sex, African American race, annual income of <$20,000, and/or unemployed due to disability). We used linear regression models and Sobel's test of mediation to examine hypotheses. RESULTS: The mean ± SD number of social disadvantages was 1.3 ± 1.0. Cumulative disadvantage was significantly associated with higher perceived discrimination, pain, and depression (P < 0.001 for all). Perceived discrimination significantly mediated the association between cumulative disadvantage and depression symptoms (Z = 3.75, P < 0.001) as well as pain severity (Z = 2.24, P = 0.025). CONCLUSION: Perceived discrimination is an important psychosocial stressor that contributes to worsening OA-related mental and physical health outcomes, with greater effects among those from multiple socially disadvantaged groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".