Mental health disorders research in the countries of the Organisation of Islamic Cooperation (OIC), 2008–17, and the disease burden: Bibliometric study
Bibliographic record
Abstract
The 57 countries of the Organisation of Islamic Cooperation are suffering from an increasing burden from mental health disorders. We investigated their research outputs during 2008-17 in the Web of Science in order to compare them with the burden from different mental health disorders and in different countries. The papers were identified with a complex filter based on title words and journals. Their addresses were parsed to give fractional country counts, show international collaboration, and also reveal country concentration on individual disorders and types of research. We found 17,920 papers in the decade, with output quadrupling. Foreign contributions accounted for 15% of addresses; they were from Europe (7%), Canada + USA (5%) and elsewhere (3%). They were much greater for Qatar and Uganda (> 60%), but less than 10% for Iran and Turkey. Schizophrenia and bipolar disorder were over-researched, but suicide and self-harm were seriously neglected, relative to their mental health disorder burdens. Although OIC research has been expanding rapidly, some countries have published little on this subject, perhaps because of stigma. Turkey collaborates relatively little internationally and as a result its papers received few citations. Among the large OIC countries, it has almost the highest relative mental health disorders burden, which is also growing rapidly.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.143 | 0.256 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".