Education and Employment Participation in Young Adulthood: What Role Does Arthritis Play?
Bibliographic record
Abstract
OBJECTIVE: To examine the association between arthritis diagnosis and education and employment participation among young adults, and to determine whether findings differ by self-rated health and age. METHODS: Data from the National Health Interview Survey, in the years 2009-2015, were combined and analyzed. The study sample was restricted to those ages 18-29 years, either diagnosed with arthritis (n = 1,393) or not (n = 40,537). The prevalence and correlates of employment and education participation were compared by arthritis status. Demographic characteristics, social role participation restrictions, health factors, and health system use variables were included as covariates. Models were stratified for age (18-23 versus 24-29 years) and self-rated health. Weighted proportions and univariate and multivariate associations were calculated to examine the association between arthritis and education and employment participation. RESULTS: Respondents with arthritis were more likely to be female, married, and report having more social participation restrictions, fair/poor health, and more functional limitations than those without arthritis. In multivariate models, arthritis was significantly associated with lower education (prevalence ratio [PR] 0.75 [95% confidence interval (95% CI) 0.57-0.98]) and higher employment participation (PR 1.07 [95% CI 1.03-1.13]). Additional stratified analyses indicated an association between arthritis diagnosis and greater employment participation in those ages 18-23 years and reporting higher self-rated health. CONCLUSION: Young adults with arthritis may be transitioning into employment at an earlier age than their peers without arthritis. To inform the design of interventions that promote employment participation, future research on the education and employment experiences of young adults with arthritis is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".