P2-257 Social determinants and high risk behaviours in drug users under methadone treatment in Golestan province, North Iran
Bibliographic record
Abstract
Introduction Drug users' health is a product of drug-effects and risk behaviours. Social determinants can shape some health behaviours. There has been little research on social determinants and risk behaviours related to drug use in Iran. We aimed to estimate this in patients undergoing methadone treatment in north Iran. Methods 400 addicts were recruited from methadone clinics in 2009–2010. A self-designed questionnaire with excellent reliability was used to determine those with and without high risk behaviours. ORs with 95% CIs were estimated by logistic regression. Ethics approval was obtained from Tehran University. Results There was significant difference between the two study groups (with and without high risk behaviours) in economic status, drug type, administration route, age, and drug abuse onset age. Education level was significantly lower in women. One quarter experienced homeless, 62% had no support from any insurance or supportive organisations, only 26% had constant employment and over 50% had prison history. 12.5% were injection users and 14% shared syringes. One quarter reported high risk sexual behaviour and 69.4% had not used condoms in last their last sex encounter (significantly lower in women). A decrease of one year in age was associated with decreased drug use onset age and increased sexual risk behaviours by 6%(AOR=0.94, 95% CI 0.91 to 0.98) and10% (AOR=0.91, 95% CI 0.85 to 0.97) respectively. Poor economic status reduced risk sexual behaviour (AOR=0.35, 95% CI 0.13 to 0.96). Prison history increased injection behaviour more than twice (AOR=2.89, 95% CI 1.4 to 5.95). Conclusions These findings illustrate that interventions are needed in young heroin users even in those with a good economic state.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".