Integrating Risk Factors for Substance Use Disorders: Applying a Gender Lens
Bibliographic record
Abstract
The present study sought to understand the relationship between complex, multifaceted risk factors of substance use, including childhood maltreatment and coping strategies, and how they vary across gender and mental health outcomes including anxiety, depression, trauma, and quality of life. A sample of 257 treatment seeking individuals diagnosed with substance use disorder completed the following measures: the Diagnostic Assessment Research Tool, the Childhood Trauma Questionnaire, Coping Orientation to Problems Experienced Inventory, Alcohol Use Disorder Identification Test, Drug Use Disorder Identification Test, BEM Sex Role Inventory, World Health Organization Quality of Life, Generalized Anxiety Disorder 7, Patient Health Questionnaire-9, and PTSD Checklist for DSM-5. A latent profile analysis demonstrated that a four-profile model best fit the data, resulting in High Abuse, Low Emotional and Physical Abuse, High Emotional and Physical Abuse, and Low Coping profiles. No gender or substance use disorder severity differences were found between profiles. The High Abuse and High Emotional and Physical Abuse profiles had significantly worse mental health outcomes than the other profiles. In summary, within a sample of participants with a substance use disorder diagnosis, three of four latent profiles endorsed similar coping strategies. Further, profiles with higher levels of childhood maltreatment also reported worse mental health outcomes including, depression, trauma, and quality of life. These results may indicate that experience of childhood maltreatment is most useful to stratify individuals in clinical settings, particularly given that these profiles differed on key mental health outcomes. Gender identity and gender role differences were not indicated between profiles or outcomes. Areas for future research are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".