Mental health research in the Arab region in response to the COVID‐19 pandemic: a scoping review
Bibliographic record
Abstract
Background: The ongoing pandemic has led to a global surge in coronavirus disease 2019 (COVID-19)-related mental health research. However, most related publications come from Western countries or China, and their findings cannot always be extrapolated to Arab countries. Aims: This study provides a quantitative and qualitative analysis of mental health research pertaining to Arab countries' response to the COVID-19 pandemic. Methods: A scoping review of the World Health Organization (WHO) COVID-19 database for publications on mental health was conducted by authors affiliated with Arab institutions, including articles from inception to 24 October 2020. The included publications were evaluated for their national distribution, international collaboration, publication type, and main research themes. Methodological quality analysis of the included research studies was performed using the original and modified versions of the Newcastle-Ottawa Scale. Results: In total, 102 articles were included in this study, averaging 4.6 articles per Arab country. Most of the articles emerged from the Kingdom of Saudi Arabia, Jordan, and Egypt. A majority of publications demonstrated international collaboration. Most of the publications were original research studies and cross-sectional in design. The predominant research theme was examining the pandemic's mental health effects on the general population and healthcare workers. Only 28.0% of the studies were of high methodological quality, whereas 41.5% were moderate and 30.5% were low in quality. Conclusions: Mental health research in response to the COVID-19 pandemic in the Arab region has quantitative and qualitative shortfalls. Arab institutions need to respond to the pandemic promptly in order to address the delineated research gap and to generate higher-quality research output.
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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 | 0.023 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".