Cognitive Functioning and Work in People With Severe Mental Illness Living in Urban and Rural Areas in India
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESES: Evidence on the role of cognition in employment of people with severe mental illness (SMI) living in India and other developing countries is limited. This study examined the relationship between cognitive functioning and work in people with SMI living in urban and rural areas in India. STUDY DESIGN: Cognition (evaluated with the Montreal Cognitive Assessment: MoCA) and vocational functioning were assessed in 340 persons with SMI (59% schizophrenia-schizoaffective) receiving private psychiatric outpatient treatment at two hospitals in western India. STUDY RESULTS: Participants with higher levels of cognitive functioning were more likely to be employed than those with lower levels, including both those living in urban and rural areas. Among employed participants, better cognitive functioning was associated with working at more complex and skilled jobs that paid higher wages. There were no differences in cognitive functioning between participants working for a family-run business (eg, a farm) vs. an independent employer, suggesting that families operating such businesses did not provide more work accommodations for cognitive impairment to their relatives with SMI than independent employers. CONCLUSIONS: Impaired cognitive functioning is an important predictor of unemployment in people with SMI in both rural and urban regions in India. Providing interventions for enhancing cognitive functioning may increase the ability of unemployed people with SMI in developing countries to work for both family-operated businesses and independent employers, thereby improving their economic standing and the welfare of their families.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".