Is There A Long Run Nexus Among Mental Disorder And Socio-Economic Indicators? : Experiences From An Econometric Study Across 40 Countries
Bibliographic record
Abstract
Are there evidences of an association between poor mental health and the experience of poverty and socio-economic deprivation? To explore it, we try to relate all sorts of mental disorders with the per-capita GDP (PCGDP), the level of per-capita CO2 emissions as a measure of pollution (PCCO), usage of Internet (IU) as a measure of social behaviour, and Globalization Index (GI), for all the major countries in the world. Applying Vector Autoregression (VAR) model the results reveal that most of the high income countries in the selection have produced the result that mental disorder is cointegrated to the four socio economic indicators. The short run causality tests unambiguously backs up the sustainability of the long run cointegration relations derived for countries like Argentina, Australia, Canada, France, Germany, and UAE. Hence, mental disorder is not a problem to the lower income countries but to the high income countries as well.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".