Using the PRECEDE Model in Understanding Determinants of Quality of Life Among Iranian Male Addicts
Bibliographic record
Abstract
Quality of Life (QOL) in opiate-addicted patients who are receiving methadone maintenance therapy is one of the important issues to be considered in the treatment of addiction. To determine a needs assessment using the PRECEDE model to find out factors related to QOL among Iranian male opiate addicts. This cross-sectional study was conducted in Kermanshah, Iran in 2013. A total of 762 male opiate addicts, who were referred to addiction treatment centers for receiving methadone maintenance treatment, were randomly selected to participate voluntarily in the study. SF-12, predisposing factors, enabling factors, reinforcement factors, and methadone maintenance treatment intention were used to find the related factors. Data were analyzed by the SPSS software (ver. 21.0) using the t-test, one-way analysis of variance (ANOVA), bivariate correlations, and linear regression at 95% significant level. Linear regression analysis showed the determinant variable accounted for 17% of the variation in QOL. Our findings suggest, providing social support for addicts could be beneficial results for the increasing quality of life among them.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".