Comparative Study: Mental Health of Indian Citizens and Indians Overseas
Bibliographic record
Abstract
India is becoming the leading nation of immigrant origin in. Every year many people emigrate from India to other countries. Immigration is not easy and comes with its own troubles and has a profound impact on mental health. The aim of the study was to compare the mental health of Indian citizens and Indians overseas (NRIs and POIs). Samples (N=100) were collected from people living in India, Canada and USA falling in the age group of 21-75years. Two self-rated scales Beck depression inventory and Zung self-rated anxiety scale was used to collect the data. The results show that a total of 23% of the samples were suffering from depression. 16% were dealing with mild mood disturbances, 2% with borderline clinical depression, 3% moderate depression, 1% severe depression and 1% extreme depression as self-rated depression and for anxiety the results show that 27% of the sample population were suffering from anxiety issues. 25% had minimal to moderate anxiety levels and 2% had marked to severe anxiety levels as self-rated anxiety. ANOVA analysis was used to compare the two groups it showed that over all there was no significant difference between the two groups. Although while comparing the population in age groups it was found 21-30year old Indian citizen mental health was better than Indians overseas. No significant difference was found in the age groups 330-50years and 50and above.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".