Psychological impacts of Intentional Non-Medical Fentanyl Use Among People Who Use Drugs: A Systematic Review
Bibliographic record
Abstract
Introduction The use of non-medical fentanyl and structurally related compounds has changed drastically over the last ten years. Community members working with individuals who use fentanyl intentionally currently struggle with the rapidly evolving drug markets and patterns of use, thereby failing to adapt treatment approaches and harm reduction strategies to individuals with severe opioid use disorder (OUD) and concurrent psychiatric disorders. Objectives This systematic review aims to evaluate intentional fentanyl among PWUD by summarizing demographic variance, concurrent disorders, and resulting patterns of use. Methods The search strategy in this study was developed with a combination of free text keywords and Mesh and non-Mesh keywords, and adapted with database-specific filters to Ovid MEDLINE, Embase, Web of Science, and PsychINFO (May 2021). The search results resulted in 4437 studies after de-duplication, of which 132 were selected for full-text review. A total of 42 articles were included in this review. Results It was found that individuals who use fentanyl intentionally were more likely to be young, male, and Caucasian. Individuals who intentionally use fentanyl were more commonly homeless, unemployed or working illegally, and live-in cities. Independent correlates of any purposeful fentanyl use included moderate/severe depression. Conclusions Individuals who intentionally use fentanyl are more likely to report injection drug use and polysubstance use, including cocaine use, heroin use, and methamphetamine use. Among PWUD, individuals who intentionally use fentanyl have the most severe substance use patterns, the most precarious living situation, and the most extensive overdose history and higher proportion of ever having a mental health diagnosis. Disclosure No significant relationships.
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 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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".