Meta‐analysis of predictors of favorable employment outcomes among individuals with bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: Although several studies have reported on predictors of employment in individuals with bipolar disorder (BD), the magnitude of the impact of these variables is unclear as no previous studies have estimated the collective effect sizes (ESs). The present meta-analysis estimated ESs and explored which of these variables are associated with positive employment outcomes. METHODS: We searched for articles published between 2000 and 2011 that reported associations between sociodemographic, clinical, psychosocial, and/or cognitive variables with employment outcomes in BD. Of the 781 articles identified, 22 met the inclusion criteria and were included in the final analysis (n = 6,301). Weighted correlation coefficients (r-index) were computed as ESs for each of the predictor variables, which were grouped into six categories: cognitive performance, symptomatology, sociodemographic factors, course of illness, clinical variables, and other personal factors. The overall ES (Rw) was estimated by assuming random-effect models. Sensitivity analyses were also performed to determine the robustness of the findings. RESULTS: Significant predictors of favorable employment outcomes included: cognitive performance (e.g., verbal memory, Rw = 0.33; executive function, Rw = 0.26), sociodemographic factors (e.g., years of education, Rw = 0.23), course of illness (e.g., number of lifetime psychiatric hospitalizations, Rw = -0.35), symptomatology (e.g., depression, Rw = -0.25), and other personal factors (e.g., personality disorder, Rw = -0.49). CONCLUSIONS: Overall, the cognitive performance and course of illness had larger average ESs than symptomatology or sociodemographic factors on favorable employment outcomes. These findings may help to guide the design of more effective work interventions for people with BD.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.040 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".