Exploring the Relationship between Drug Addiction and Quality of Life in Herat, Afghanistan: A Cross-sectional Study
Bibliographic record
Abstract
Background: Addiction is a global public health problem, with over 36 million people suffering from drug-use disorders. Afghanistan, the world’s leading opium producer, has high rates of drug use owing to the easy access to drugs in this country. This study aimed to investigate drug users’ quality of life in Herat, Afghanistan, and identify the factors affecting it. Methods: This cross-sectional study examined health-related quality of life at six rehabilitation camps in Herat, Afghanistan, from March to July 2019, using the short form-36 questionnaire (SF-36). Data collected through interviews were analyzed using SPSS software (version 25). Findings: A total of 240 participants from six rehabilitation camps in Herat, Afghanistan participated in this study. The majority of participants (80%) rated their overall health as "good" or "very good". Men had higher average scores for mental health and vitality than women and those aged 30-39 had the highest quality of life. Statistically significant differences were found in bodily pain (P=0.038), vitality (P=0.042), and social functioning (P=0.046) among users of different types of drugs. Opium abusers had the highest scores for the physical and mental components, followed by heroin, methamphetamine, hashish, and crack abusers. Conclusion: This study explored the relationship between drug addiction and quality of life in Herat, Afghanistan. The findings showed that young adults were more vulnerable to drug use and male addicts and opium users had the highest quality of life. This study can inform the development of effective rehabilitation programs but more research is needed for addiction treatment strategies.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".