Depressive Symptoms and the Arthritis–Employment Interface: A Population‐Level Study
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between depressive symptoms, arthritis, and employment, and to determine whether this relationship differs across young, middle-age, and older working-age adults with arthritis. METHODS: Data from the US National Health Interview Survey from 2013-2017 were analyzed. Analyses were restricted to adults with doctor-diagnosed arthritis of working age (ages 18-64 years) with complete data on depressive symptoms (n = 11,380). Covariates were sociodemographic information, health, and health system utilization variables. Employment prevalence was compared by self-reported depressive symptoms. We estimated percentages, as well as univariable and multivariable logistic regression models, to examine the relationship between depression and employment among young adults (ages 18-34 years), middle-age adults (ages 35-54 years), and older adults (ages 55-64 years). RESULTS: Among all working-age US adults with arthritis, the prevalence of depressive symptoms was 13%. Those reporting depressive symptoms had a higher prevalence of fair/worse health (60%) and arthritis-attributable activity limitations (70%) compared to those not reporting depression (23% and 39%, respectively). Respondents with depressive symptoms reported significantly lower employment prevalence (30%) when compared to those not reporting depressive symptoms (66%) and lower multivariable-adjusted association with employment (prevalence ratio 0.88 [95% confidence interval (95% CI) 0.83-0.93]). Middle-age adults reporting depression were significantly less likely to be employed compared to their counterparts without depression (prevalence ratio 0.83 [95% CI 0.77-0.90]); similar but borderline statistically significant relationships were observed for both young adults (prevalence ratio 0.86 [95% CI 0.74-0.99]) and older adults (prevalence ratio 0.94 [95% CI 0.86-1.03]). CONCLUSION: For adults with arthritis, depressive symptoms are associated with not participating in employment. Strategies to reduce arthritis-related work disability may be more effective if they simultaneously address mental health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".