Personal, Familial and Environmental Determinants of Drug Abuse: A Causal-Comparative Study
Bibliographic record
Abstract
AIMS: Two purposes were followed in this study: 1) comparing case and control group in eight factors separately and 2) performing a multivariate analysis for identifying risk and protective factors in relation to drug abuse. METHODS: A casual-comparative study was conducted to investigate the study goals. Fifty Cases in a convenient sampling of addicts referring to addiction withdrawal centers and fifty eligible controls (recruited in a randomly sampling) were identified. One-sample independent T-Test for a univariate and Logistic regression model for a multivariate was conducted. RESULTS: Univariate analysis: addicted group compared with control group, in terms of aggression, easy access to drugs and depression had higher scores and of other factors (self-esteem, religious affiliation, socioeconomic status, family environment and responsibility) cases had lower scores (p<0.05). Multivariate analysis: Easy access to drugs and depression identified as risk factors (OR>1) and high self-esteem, family socioeconomic status and responsibility as protective (OR<1). CONCLUSIONS: Addiction is a multivariate phenomenon and before any intervention, we have to consider personal, familial and environmental factors and separate subjects by them. We can't give all of addicts the same prescription and follow a drug therapy approach to treat them. Any addict has a unique profile that should be taken into consideration.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".