A systematic review of gender-responsive and integrated substance use disorder treatment programs for women with co-occurring disorders
Bibliographic record
Abstract
Background: Integrated and gender-responsive interventions, designed to target co-occurring substance use and psychiatric disorders in women, may be effective in addressing gender-specific challenges.Objectives: This systematic review aims to identify integrated gender-responsive substance use disorder treatments for women, summarize evaluations of these treatments, and address gaps in the literature.Methods: We searched PsycINFO, PubMed, and MEDLINE on September 24, 2021, and March 10, 2022. Included articles were randomized-controlled trials, secondary analyses of naturalistic studies, or open-label studies of integrated and gender-responsive treatments from any year that assessed both substance use and mental health/trauma outcomes.Results: We identified N = 24 studies (participants = 3,396; 100% women) examining Seeking Safety, Helping Women Recover and Beyond Trauma, A Woman’s Path to Recovery, Modified Trauma Recovery and Empowerment Model (TREM), Breaking the Cycle, VOICES, Understanding and Overcoming Substance Misuse, Women’s Recovery Group, Female Specific Cognitive Behavioral Therapy, and Moment by Moment in Women’s Recovery. Across treatments there were significant improvements over time; Seeking Safety, Helping Women Recover, and TREM were associated with significantly better substance use and mental health outcomes relative to the comparison groups.Conclusions: Integrated gender-responsive treatments are a promising approach to treating women with co-occurring substance use and mental health concerns, and broad clinical implementation stands to benefit women. However, there remains a lack of studies evaluating substance use treatments in women with severe mental illness (e.g., psychotic-spectrum disorders) who differ in their needs and capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".