Mental health status, health service utilization, drug use behaviors associated with non-fatal overdose among people who use illicit drugs: A meta-analysis
Bibliographic record
Abstract
Background The present study aims to determine mental health conditions, service use determinants, and drug use behaviors associated with non-fatal overdose among people who use illicit drugs (PWUIDs).Methods We searched for studies in English published before February 1, 2021, on PubMed, Scopus, Cochrane, and Web of Science to identify primary studies on the factors associated with non-fatal overdose among PWUIDs. After reviewing for study duplicates, the full-text of selected articles were assessed for eligibility using Population, Intervention, Comparator, and Outcomes (PICO) criteria. The present study applied OR to measure the effect size with 95% CI. For data analysis, R 3.5.1 with the “meta” package was used to conduct the meta-analysis.Results After a detailed assessment of more than 13,845 articles, a total of 60 studies met the eligibility criteria. We found that non-fatal overdose was independently and positively associated with various factors considered and was, as expected, most strongly associated with being male, needs help injecting, needle sharing, overdose experience in past 12 months, alcohol abuse, had mental health diagnosis, depression, and anxiety disorders, suicide (ideation or attempt), inpatient detoxification, benzodiazepine use, and emergency department visit or hospitalization.Conclusion The findings of the current meta-analysis support the requirement to improve suitable harm reduction strategies for drug users, such as peer-based overdose management.Abbreviations: PWUIDs: People who use illicit drugs; WHO: World Health Organization; CI: Confidence intervals; NOS: Newcastle-Ottawa Scale; OR: Odds ratio; PICO: Population, Intervention, Comparator, Outcomes; PRISMA: Protocols of Systematic Reviews and Meta-Analyses; PWIDs: people who inject drugs; WHO: World Health Organization; SIF: Safer Injection Facility
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".