Social workers, including social work researchers, are called on to challenge social injustices and pursue social change. Research has shown a strong association between trauma and adversity and marginalized populations, exposing the unequal distribution of trauma and its effects throughout society. Given the focus on marginalized populations in social work, the social justice orientation of the field, and the intersection of trauma with marginalized populations, a framework to guide social work researchers in conducting trauma-informed, socially just research with marginalized populations is warranted. Therefore, this article provides a framework integrating trauma theory, trauma-informed principles, and intersectionality as a guide for social work research. The proposed framework is illustrated using a case study of low-income, predominantly African American men recruited from a criminal justice setting, acknowledging facilitators and barriers to implementation. The article concludes with a discussion of the implications for researchers and doctoral student training.

Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.

Introduction Women with substance use disorders enter treatment with limited personal network resources and reduced recovery support. This study examined the impact of personal networks on substance use by 12 months post treatment intake. Methods Data were collected from 284 women who received substance abuse treatment. At six month follow up, composition, support availability and structure of personal networks were examined. Substance use was measured by women’s report of any use of alcohol or drugs. Hierarchical multivariate logistic regression was conducted to examine the contribution of personal network characteristics on substance use by 12 months post treatment intake. Results Higher numbers of substance using alters (network members) and more densely connected networks at six month follow-up were associated with an increased likelihood of substance use by 12 months post treatment intake. A greater number of isolates in women’s networks was associated with decreased odds of substance use. Women who did not use substances by 12 months post treatment intake had more non-users among their isolates at six months compared to those who used substances. No association was found between support availability and likelihood of substance use. Conclusions Both network composition and structure could be relevant foci for network interventions e.g. helping women change network composition by reducing substance users as well as increasing network connections. Isolates who are not substance users may be a particular strength to help women cultivate within their network to promote sustained sobriety post treatment.

Background Early identification of individuals at high risk for alcohol use disorder (AUD) coupled with prompt interventions could reduce the incidence of AUD. In this study, we investigated whether Polygenic Risk Scores (PRS) can be used to evaluate the risk for AUD and AUD severity (as measured by the number of DSM‐5 AUD diagnostic criteria met) and compared their performance with a measure of family history of AUD. Methods We studied individuals of European ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA). DSM‐5 diagnostic criteria were available for 7203 individuals, of whom 3451 met criteria for DSM‐IV alcohol dependence or DSM‐5 AUD and 1616 were alcohol‐exposed controls aged ≥21 years with no history of AUD or drug dependence. Further, 4842 individuals had a positive first‐degree family history of AUD (FH+), 2722 had an unknown family history (FH?), and 336 had a negative family history (FH−). PRS were derived from a meta‐analysis of a genome‐wide association study of AUD from the Million Veteran Program and scores from the problem subscale of the Alcohol Use Disorders Identification Test in the UK Biobank. We used mixed models to test the association between PRS and risk for AUD and AUD severity. Results AUD cases had higher PRS than controls with PRS increasing as the number of DSM‐5 diagnostic criteria increased (p‐values ≤ 1.85E−05) in the full COGA sample, the FH+ subsample, and the FH? subsample. Individuals in the top decile of PRS had odds ratios (OR) for developing AUD of 1.96 (95% CI: 1.54 to 2.51, p‐value = 7.57E−08) and 1.86 (95% CI: 1.35 to 2.56, p‐value = 1.32E−04) in the full sample and the FH+ subsample, respectively. These values are comparable to previously reported ORs for a first‐degree family history (1.91 to 2.38) estimated from national surveys. PRS were also significantly associated with the DSM‐5 AUD diagnostic criterion count in the full sample, the FH+ subsample, and the FH? subsample (p‐values ≤6.7E−11). PRS remained significantly associated with AUD and AUD severity after accounting for a family history of AUD (p‐values ≤6.8E−10). Conclusions Both PRS and family history were associated with AUD and AUD severity, indicating that these risk measures assess distinct aspects of liability to AUD traits.

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.