A comparative review of the epidemiology of mental disorders in Australia and India
Bibliographic record
Abstract
Mental illness and substance use disorders have been increasing worldwide. Mental illness has a significant impact upon the lives of the individual as well as their loved ones. Mental disorders are known to result in a high level of disability. This article provides a comparative review of the epidemiology of mental disorders in Australia and India, summarizing and comparing prevalence rates in both countries based upon available data. Overall, it is evident that Australia has higher prevalence rates of mental disorders than India, across most diagnostic groups. Australia has the highest prevalence of anxiety disorders whereas India has the highest prevalence of substance use disorders; including tobacco use disorders. The next most prevalent mental disorders in India are depressive disorders. However, there are demographic parameters such as gender and age as well as service-provision differences across the countries that need to be factored into any interpretation of the data. There are also problems associated with different diagnostic instruments with language and cultural nuances that may impact comparisons. We suggest that a joint epidemiological survey between the two countries would help better understand and delineate the key similarities pertaining to the epidemiology of mental disorders in Australia and India. This will in turn assist with the development of policy and treatment of mental disorders.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".