The impact of family systems and social networks on substance use initiation and recovery among women with substance use disorders.
Bibliographic record
Abstract
OBJECTIVE: While social networks influence individuals with substance use disorders (SUDs), the mechanisms for such influence are under-explored among women who use drugs. This study triangulates the perspectives of criminal justice professionals, SUD treatment professionals, and women with past and current experiences with substance use to explore these dynamics. METHOD: = 10) who work with women with opioid use disorder. Interviews centered around participants' backgrounds, perceived barriers and facilitators to medications for opioid use disorder (MOUD) treatment, and gender-specific issues in MOUD treatment. All interviews were audio-recorded, transcribed, and deidentified. We used a four-step qualitative data analysis process to code transcripts. RESULTS: Across these participants' accounts, we identified mechanisms by which women's social networks influenced their opioid use trajectories: intergenerational substance use, family support and strain, intimate partner influence, and peer support and pressure. Overall, the emergent themes in the present study reflect the embedded nature of support within social systems. Women who had access to and engaged with various forms of social support tended to be those who use/used MOUD and self-identified as in recovery. CONCLUSIONS: Combining MOUD treatment with psychosocial interventions allows women to heal from trauma, learn effective coping skills, and receive valuable resources to support recovery. Interventions focusing on family resilience and peer recovery support can disrupt the cycle of addiction and promote MOUD treatment success. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".