Demographic and clinical associations to employment status in older‐age bipolar disorder: Analysis from the <scp>GAGE‐BD</scp> database project
Bibliographic record
Abstract
OBJECTIVE: The current literature on employment in older adults with bipolar disorder (OABD) is limited. Using the Global Aging and Geriatric Experiments in Bipolar Disorder Database (GAGE-BD), we examined the relationship of occupational status in OABD to other demographic and clinical characteristics. METHODS: Seven hundred and thirty-eight participants from 11 international samples with data on educational level and occupational status were included. Employment status was dichotomized as employed versus unemployed. Generalized linear mixed models with random intercepts for the study cohort were used to examine the relationship between baseline characteristics and employment. Predictors in the models included baseline demographics, education, psychiatric symptom severity, psychiatric comorbidity, somatic comorbidity, and prior psychiatric hospitalizations. RESULTS: In the sample, 23.6% (n = 174) were employed, while 76.4% were unemployed (n = 564). In multivariable logistic regression models, less education, older age, a history of both anxiety and substance/alcohol use disorders, more prior psychiatric hospitalizations, and higher levels of BD depression severity were associated with greater odds of unemployment. In the subsample of individuals less than 65 years of age, findings were similar. No significant association between manic symptoms, gender, age of onset, or employment status was observed. CONCLUSION: Results suggest an association between educational level, age, psychiatric severity and comorbidity in relation to employment in OABD. Implications include the need for management of psychiatric symptoms and comorbidity across the lifespan, as well as improving educational access for people with BD and skills training or other support for those with work-life breaks to re-enter employment and optimize the overall outcome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".