The Arab Region's Contribution to Global Mental Health Research (2009–2018): A Bibliometric Analysis
Bibliographic record
Abstract
Background: Mental health research output in the Arab region is increasing, yet little is known about its recent landscape. This study provides a bibliometric analysis of mental health research in all 22 Arab countries over the past decade. Method: We used 760 journals and numerous keywords to search for articles published between 2009 and 2018 by individuals affiliated with institutions located in the Arab region. We analyzed data within Arab countries and between Arab and non-Arab countries. Results: We found that research output in the Arab world has increased by almost 160% in the past ten years, in comparison to 57% for the rest of the world. The quality of publications has also steadily improved, and so did international collaboration. Despite the progress, the number of articles per capita remains remarkably lower for the Arab world compared to the rest of the world. Also, the majority of articles continue to emanate from a limited number of countries (Egypt, Saudi Arabia, and Lebanon) and institutions within these countries. Mental health research topics in the Arab region are similar to those found in low- and middle-income countries of Africa, Asia, Latin America, and the Caribbean. Conclusion: The region needs to invest more in mental health research to close the gap with other medical and healthcare research areas and with the rest of the world. The region also needs to increase its international collaboration and research training to produce higher-quality studies, attract more funding, and publish more in top journals. As the region’s population continues to face increasing trauma as a result of war and terrorism, among others, the field is afforded an opportunity to establish a major standing in the healthcare domain. Researchers are uniquely poised to use their body of research evidence to effectively help people reengage with their environments and return to daily life activities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.172 | 0.219 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".