Factors predicting employment status among persons with schizophrenia: A cross-sectional study from Chennai, India
Bibliographic record
Abstract
BACKGROUND: Even among other mental disorders, a diagnosis of schizophrenia is associated with an abnormally low employment rate. However, those who can find employment report mental health improvements and diminishing symptoms. AIMS: In this cross-sectional study, we analyzed a variety of sociodemographic factors between groups of schizophrenia-diagnosed employed and unemployed individuals to attempt to determine any causal relationships. METHODS: A group of 52 employed and 48 unemployed individuals from the same outpatient hospital were surveyed. Patients were asked about their sociodemographic background and employment history, as well as subjected to a variety of tests to quantify critical aspects of their symptomatology. These included the Positive and Negative Syndrome Scale (PANSS), Social and Occupational Functioning Assessment Scale (SOFAS), and Personal and Social Performance Scale (PSP). The Calgary Depression Scale for Schizophrenia (CDSS) and Hamilton Anxiety Rating Scale (HAM-A) were administered to assess comorbid depression and anxiety. Lastly, the Simpson Angus Scale (SAS) measured any extrapyramidal side effects caused by the patients' medications. RESULTS: = .001). Based on these results, the creation of peer support systems at work through fostering inclusive, well-informed, and destigmatized environments should be employers' predominant focus. CONCLUSION: Future studies conducted longitudinally can strengthen the conclusions found and confirm the optimal manners in which to address the matter of aiding the integration of schizophrenic and similarly symptomatic individuals into the labor force.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".