Case finding in integration of Mental Health Services into Primary Health Care System: systematic review of the studies conducted in Iran in recent two decades
Bibliographic record
Abstract
Objective: This study aims at conducting a systematic review of the researches performed to determine case finding rates throughout the integration of Mental Health Services into PHC over recent twenty years . Method : Through electronic search, major national and international databases including Pubmed, PsychInfo, and EMBase were investigated. All original studies and researches in Persian or English, which had described psychiatric case finding in the PHC, classified as severe and mild mental disorders,epilepsy, mental retardation, and other disorders, were included in the study and were qualitatively assessed. Subsequent to data extraction, heterogeneity test was carried out on all of the studies and each subgroup. Meta-analysis was not applicable as a result of the wide range and heterogeneity of the reported results. Results: Overall, ten studies were included. Case finding rate ranged from 0.07 to 2.04 per thousand for severe mental disorders, 0.5 to 7.6 per thousand for mild mental disorders, 0.5 to 3.9 per thousand for epilepsy, and 0.64 to 3.94 per thousand for mental retardation. Conclusion: Case finding rates reported in the selected studies are highly different from the prevalence of the disorders throughout the ountry. It seems that the program has been functioning more effectively in case of some of the disorders such as mental retardation, while it has been less efficient in finding mild mental disorder cases. These results reflect the fact that despite its partial achievements in the field of case finding, the integration program is still far from the desirable rates and there is need for revision of its content of the integration program especially screening and diagnostic tools, training contents, and implementation methods.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".