Predicting the Risk of Opioid Use Disorder Based on Early Maladaptive Schemas
Bibliographic record
Abstract
Substance use is a globally devastating social problem. Early maladaptive schemas (EMSs) are inefficient mechanisms leading directly or indirectly to psychological distress. The current study aimed to assess the role of EMSs in predicting opioid use disorder. The cross-sectional study was conducted in 2013 in Bojnurd at northeast of Iran on 60 male opioid users who received Methadone Maintenance Treatment (MMT) and 60 control males. The opioid users were selected randomly from MMT clinics and control subjects were selected and matched with opioid users using demographic variables. The subjects completed the Young Schema Questionnaire-Short Form (YSQ-SF). Except for SS (self-sacrifice), EG (entitlement/grandiosity), US (unrelenting standards), and FA (Failure to Achieve), the mean of other maladaptive schemas in the opioid user group were significantly higher than that of the control group, adjusted for multiple comparisons. Multivariate analysis of variance (MANOVA) indicated significant differences in maladaptive schemas between the two groups. Logistic regression identified that Emotional Deprivation, Mistrust/Abuse, and Unrelenting Standards can predict opioid use. As a result, the risk of opioid-related disorders in people with higher YSQ-SF scores in these schemas is higher. The findings conclude that the existence of underlying EMS may constitute a vulnerability factor for developing opioid use disorders later on in life. Provided the vast amount of scientific literature in evidence-based treatments focusing on EMSs, maladaptive schemas and related core beliefs can be detected and treated in adolescence to prevent the enactment of the schema and psychological distress likely to induce opioid use.
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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 | 0.003 |
| 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 | 0.001 |
| 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".