Cognitive Impairments and Its Related Factors in People Who Injects Drugs in Iran
Bibliographic record
Abstract
Background: One of the most common psychological problems among people who use drugs (PWUD) is cognitive impairments (CIs). Several studies show that the prevalence of cognitive impairments in PWUD is higher than in the general population. Objectives: The aim of the current study was to investigate the prevalence of CI and its related factors among people who inject drugs (PWID) in Iran. Methods: A cross-sectional study was designed among PWID in 2023. About 410 PWID were recruited using snowball and convenience sampling in four provinces in Iran. Demographic and clinical data, as well as the status of cognitive impairments, were collected using bio-behavioral and Montreal Cognitive Assessment questionnaires, respectively. We examined the relationship between all independent variables and CIs status using bivariate logistic regression. Results: The prevalence of CI among participants was 28% (95% confidence interval (CI): 22% - 32%). Behaviors associated with an increased risk of CIs included the length of injecting drug use [adjusted odds ratios (aOR) = 2.7, P = 0.01], methamphetamine use (aOR = 2.8, P < 0.05), and alcohol use (aOR = 2.8, P < 0.05). Conclusions: Methamphetamine and alcohol use were identified as the main predictors of cognitive impairments among PWID. It also seems necessary to consider new interventions in harm reduction programs to address cognitive impairments.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".