The Most Common Reasons and Incentives of Tendency to Addiction in Prisons and Rehabilitation Centres of Zahedan (Iran)
Bibliographic record
Abstract
In most European countries the ratio of addicts and normal people is 1 to 5,000, and in some third world countries such as Morocco, Egypt, and South Africa this figure is 1 to 1,000. In Iran, the situation is worse, and there is one drug addict per 100 people. The research method in this study is analytical descriptive. The study population consisted of all rehabilitated addicted men and women who were spending their time in prison. Total sample size were 134 people (99 men and 35 women). A special designed questionnaire used to collect data, which included socio-demographic characteristics. The validity of the questionnaire has content validity and for reliability, Cronbach ? was used which was 0.78. The formal years of education was 4.3 year. The average age of the first drug use was 12 years for men and 22 years for women. Sixty-nine point seven percent of men used drugs with their friends and 31.4% of women used drug with their husbands. The men more than women, single men more than married women, and illiterates or poor literates were more at risk. Most men blamed bad friends and women blamed physical and psychological problems as the cause of addiction. The singles got addicted due to bad friends and married individuals were addicted due to emotional distress. The majority of age group (27-13 years), got addicted due to bad friends and older groups addicted due to emotional distress. In other words, the older the person gets, the influence of bad friends decreases, and the effect of psychological distress due to conflict and adversity increases.
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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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".