A Comparative Study of Self-Regulation in Substance Dependent and Non-Dependent Individuals
Bibliographic record
Abstract
BACKGROUND: Several factors influence the beginning and maintenance of substance use. The purpose of this study was to examine as well as to compare 'self-regulation' in both substance dependent and non-substance dependent individuals. METHOD: In a cross-sectional study 228 (118 substance dependent and 110 with no history of using substance) participants aged 16-55 were recruited. All of the participants were asked to complete the Self-Regulation Inventory (SRI-25) and a demographic characteristics data checklist. Data was analyzed using descriptive statistics (frequency, mean and standard deviation) and the t-test. RESULTS: The results showed significant differences between substance dependent and non- substance dependent groups in all the scales of the self-regulation inventory including positive actions, controllability, expression of feelings and needs, assertiveness, and well-being seeking (p<0.01). CONCLUSION: Self-regulation and self-control skills in drug dependent individuals are lower than those without substance dependence individuals. It is concluded that substance use may related to a deficiency in self-control and regulation of feelings. Therefore, for prevention and treatment of substance dependence disorder, it is necessary to work out and exploit strategies that include the improvement of self-regulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".