Mental Health Policies: Comparative Analysis of Mental Health Systems in Iran and Six Selected Countries
Bibliographic record
Abstract
Background: Mental disorders are recognized as the main cause of disability in the world. It is estimated that mental disorders will be the second leading cause of disabilities in the world by the year 2020. Nowadays, the importance of mental health and its vast effect on the other sections of health (including physical, social, and spiritual health) is not deniable. Methods: This comparative-descriptive study intends to compare mental health systems of Iran and the selected countries in the year 2019. In order to achieve this goal, two groups of countries were selected to be compared with Iran. The first group consisted of countries similar to Iran in terms of context and texture specifications, including Turkey, Iraq, and Lebanon. Moreover, the second group included countries that are known to be successful in managing and providing health services, including Norway, Canada and Australia. In order to evaluate and compare mental health systems, the authors used the WHO Assessment Instrument for Mental Health Systems (WHO-AIMS). A summarized version of the WHO-AIMS form was implemented for collecting the data. Results: After evaluation of the data obtained by mental health assessment tool, the results for each country were summarized and presented in comparative tables. Comparing mental health systems in the selected countries and Iran showed that there are a number of differences between countries in different aspects. Conclusion: While Iran has specific mental health policies, these policies are not implemented desirably because of the lack of executive guarantees in this manner.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".