Substance use disorders in Saudi Arabia: a scoping review
Bibliographic record
Abstract
Abstract Background Substance use disorders (SUD) are mental health conditions that arise from chronic drug use. There is an increased recognition of this problem in Saudi Arabia. Objective Conduct a comprehensive review of published literature on SUD to identify knowledge gaps and to guide future research. Methods PubMed, Embase and Cochrane databases were searched with suitable keywords for SUD publications up to June 10, 2019. Eligible studies (primary research conducted in Saudi Arabia) were organized into three broad domains: (1) risk (or protective) factors of SUD, (2) perspectives on drug use of people who use drugs, and (3) impact on family. The quality of the included studies was assessed with the Newcastle-Ottawa Scale. Results Of the 113 search records, 23 were eligible for analysis (19 cross-sectional and 4 case-control). All studies were conducted in clinical settings; all but two included males only. There were 4 studies about SUD risk factors, 6 studies about the perspectives of people who use drugs, and none about family impact. None of the cross-sectional studies (0%) and 25% of case-control studies were of good quality. Conclusions The available studies were few in number, weak in methodology, and poor in quality. Quantitative as well as qualitative studies about SUD are warranted in each domain and should represent both genders.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".