Prevention Science[JOURNAL]
Sun
200 papers
Bullying, Suicide, Substance Use, and Mass Harm: Patterns of Overlapping Threats Reported to a Statewide Anonymous Technology-Facilitated Reporting System.
Thulin EJ, Goodrum S, Mather E … +1 more , Heinze J
Prev Sci · 2026 Apr · PMID 42142216 · Full text
Technology-facilitated anonymous/confidential reporting systems (TFRS; tip lines) are used in K-12 schools statewide in 34/50 states to enhance youth safety and prevent interpersonal and self-directed harm. Leveraging di...
Technology-facilitated anonymous/confidential reporting systems (TFRS; tip lines) are used in K-12 schools statewide in 34/50 states to enhance youth safety and prevent interpersonal and self-directed harm. Leveraging discreet and digitally relevant reporting modes including Apps, websites and call-in submission options, states receive thousands of tips annually representing a variety of concerns (e.g., suicidal ideation, substance use, weapons access, bullying, potential school shootings). Though overseers of TFRS systems anecdotally note that multiple concerns are often present in a single tip, the unstructured nature and volume of the tip data have been barriers to systems understanding the prevalence and patterns of overlap, and implications of multiple concerning behaviors on severity of the threat. Using mixed methods, this research team manually coded a random sample of just over 10% of tips submitted between November 2019 and May 2023 for concerning behaviors (n = 2739; coder inter-rater reliability = 0.92) from a statewide system, then utilized descriptive, bivariate and multivariable regression models and latent class analyses to provide estimates on prevalence, patterns of overlap, and differential effects of multiple concerns reported in a tip with the tip being identified as life-threatening. One-third of tips (32%) described multiple concerning behaviors. The most common concern reported in tips were suicide (21.2%), bullying (20.2%), and aggressive behaviors (19.3%). Regardless of the risk, multiple risk factors in a tip consistently increased the odds that a tip would be classified as life-threatening and require emergency responders. Latent patterns of overlap were best represented by a 6-class solution; patterns of overlap included aggressive behaviors or bullying with identity-based violence; suicide with non-school concerns and substance use; and mass-harm events with more generalized weapon concerns (not related to mass harm events) and other aggressive behaviors. This statewide tip line is used for a variety of reasons, and a third of tips contain reference to multiple concerning behaviors that exacerbated features of risk. The patterns identified in the present analysis can be used to further tailor the implementation of these systems, including informing training materials for students to maximize the effectiveness of these tools.
Evaluating Intra-household Agreement on Multi-domain Family-Level Social Determinants of Health and Exploring Individual Correlates of Agreement in Two Southern California Family Studies.
Descarpentrie A, Shah P, Esaian S … +5 more , Boutelle KN, Eichen DM, Alderete TL, Goran MI, Espinoza J
Prev Sci · 2026 Apr · PMID 42118237 · Full text
Accurate self-reported data on social determinants of health (SDoH) are essential for improving prevention initiatives. Beyond survey content and validation, deciding which household member should complete family-level S...
Accurate self-reported data on social determinants of health (SDoH) are essential for improving prevention initiatives. Beyond survey content and validation, deciding which household member should complete family-level SDoH assessments can affect data quality. Yet, few studies have explored how family members report on various SDoH, especially in the Latino/Hispanic community experiencing greater health challenges. This study not only examined intra-household agreement on family-level SDoH items but also assessed combined individual SDoH linked to agreement in Southern California, which hosts one of the largest Latino/Hispanic communities in the USA. We analyzed data from 277 adult pairs (n = 554) across Southern California who completed the National Institute on Minority Health and Health Disparities Common Data Elements questionnaire. Each respondent answered 19 household-level items across four domains: Demographics, Economics, Health and Clinical Care, and Housing. Agreement was evaluated using simple or weighted Cohen's Kappa (range, ≤ 0 to 1). Modified Poisson regression examined associations between agreement and combined individuals' health literacy, employment, sex at birth, age, birthplace, and ethnicity. Tests were corrected for multiple comparisons. Among respondents, 56.3% were women, participants' mean age was 41.8 years (standard deviation = 8.6), 93.1% identified as Latino/Hispanic, and 37% reported low health literacy. Agreement across domains varied (Kappa = 0.14-0.85), with higher agreement observed for Demographics and transportation items, and poor agreement on financial adversity, healthcare, and food insecurity items. Agreement on food insecurity items varied by dyad composition: same-sex dyads demonstrated greater agreement than mixed-sex dyads, whereas dyads in which both members identified as Latino/Hispanic showed lower levels of agreement. Variations seem to exist in how household members report on family-level SDoH, particularly for sensitive areas like finances and food access. To improve accuracy, researchers and public health professionals might consider collecting data from multiple household members while encouraging joint responses and accounting for specific when designing assessments.
Who Is Getting the Help They Need? An AI-Driven Study of Intersectional Disparities in Mental Health Service Utilization Among Young Adults with Suicidal Ideation.
Donnelly HK, Kim S, Kim S … +4 more , Crosby ES, Oquendo MA, Brown GK, Mowery DL
Prev Sci · 2026 May · PMID 42118236 · Publisher ↗
Despite the critical role of mental health services in suicide prevention, disparities in service utilization persist across various individual and social determinants of health. This study identifies key factors and int...
Despite the critical role of mental health services in suicide prevention, disparities in service utilization persist across various individual and social determinants of health. This study identifies key factors and intersectional patterns associated with mental healthcare use among young adults with past-year suicidal ideation, by developing machine learning-based artificial intelligence to improve predictive accuracy while ensuring interpretability for clinicians and policymakers. Utilizing cross-sectional data from the 2015-2020 National Survey on Drug Use and Health, we analyzed 11,018 US young adults aged 18-34 who reported past-year suicidal ideation. Random Forest and Shapley Additive Explanations identified the strongest predictors of mental healthcare utilization among 23 individual and social determinants of health. The decision tree model visualized prediction of utilization rates across intersectional characteristics. Findings indicate that depression, race/ethnicity, sexual orientation, and private health insurance were the strongest predictors of service use. Individuals without depression, males, Black, Indigenous, and People of Color, heterosexual individuals, and individuals without private health insurance were significantly less likely to seek care. These findings highlight the current intersectional disparities in mental healthcare utilization among young adults at risk of suicide. Expanding culturally competent care and promoting equitable access to mental healthcare for young adults at risk are crucial steps in addressing these disparities.
Indirect Effects of the Coping Power Intervention on Latent Suicidal Thoughts and Behaviors: an Integrative Data Analysis.
Morgan-López AA, West SG, Saavedra LM … +10 more , O'Shea NCG, McDaniel HL, Tonigan AT, Budavari AC, Ünlü A, Powell NP, Qu L, Yaros AC, Bradshaw CP, Lochman JE
Prev Sci · 2026 Apr · PMID 42113366 · Full text
There has been growing interest in preventive intervention "crossover" effects on suicidal thoughts and behaviors (STBs), in which targeting early risk factors may mitigate distal risk for STBs without STBs having been t...
There has been growing interest in preventive intervention "crossover" effects on suicidal thoughts and behaviors (STBs), in which targeting early risk factors may mitigate distal risk for STBs without STBs having been the targeted outcomes of the primary study. The present study extends an 11-study integrative data analysis of the Coping Power (CP) intervention (N = 3182) to assess indirect effects of different forms of CP on teacher- and parent-reported STBs as transmitted through different subdimensions of internalizing and externalizing problems. Compared to school-as-usual, all forms of CP (Standard/Group CP, Individual CP, CP with Mindfulness, Internet-Enhanced CP) led to reductions in parent- and/or teacher-reported youth STBs. Subgroup analyses suggested that boys benefitted from Individual CP and CP with Mindfulness mediated by reductions in aggressive behavior, whereas boys in Standard CP saw reductions in STBs mediated by reductions in conduct problems. Girls saw reductions in STBs in CP with Mindfulness mediated by reductions in anxiety. Some inferences made for individual and paths and mediation effects differed when using standard parametric approaches for inference versus bias-corrected percentile bootstrapping. These differences highlight cautions regarding statistical inference for prevention researchers who study highly skewed zero-inflated latent variables such as STBs. Findings are discussed in light of (a) earlier etiological research on biological sex-specificity in the pathways to early risk for suicide and (b) how variation in program components of CP and its adaptations may reduce STB risk across different populations, age groups, and modes of program delivery.
A Guide to Constructing Indigenous Statistical Spaces for Prevention Science Research.
de la Sierra VQ
Prev Sci · 2026 May · PMID 42101761 · Full text
Artificial intelligence (AI)-powered computational methods, such as machine learning and natural language processing, are increasingly applied in deaths of despair research among Indigenous populations. However, their ap...
Artificial intelligence (AI)-powered computational methods, such as machine learning and natural language processing, are increasingly applied in deaths of despair research among Indigenous populations. However, their application in Indigenous contexts is often constrained by epistemological misalignment, technical limitations, and ethical concerns. Integrating Indigenous Research Methodologies into AI-powered prevention science research is necessary to support Indigenous Data Sovereignty and address deaths of despair. The Indigenous Computational Approach (ICA) provides a structured reflexive protocol for constructing Indigenous Statistical Spaces that operationalize Indigenous Research Methodologies within computational workflows. ICA aligns four interdependent components: Researcher Standpoint, Indigenous Theoretical Frameworks, AI Data Analysis Technique, and Dissemination and Indigenous Governance. This protocol is supported by operational steps and an accompanying ICA Checklist. A previously published case study on the Indigenous Wholistic Factors Project illustrates the ICA in practice in the context of suicide risk modeling. The case study applied a lasso logistic regression model to structure feature selection on an Indigenous subsample of the 2019-2020 California Healthy Kids Survey (n = 2609). Ten of 17 candidate features were retained, and the model demonstrated strong discrimination (AUC = 0.87) and acceptable calibration (Brier score = 0.10). The ICA does not guarantee different empirical findings or superior model accuracy, but rather it restructures how AI models are designed, validated, and deployed for prevention science research. The ICA provides a replicable protocol for AI-powered prevention science research to support Indigenous self-determination and community-defined well-being.
Detecting Patterns of Intimate Partner Violence Using Qualitative Analyses and Machine Learning Algorithms.
Zhang Y, Fang J, Krishnakumar A
Prev Sci · 2026 May · PMID 42090062 · Publisher ↗
Intimate partner violence (IPV) survivors increasingly use social media platforms to share their experiences and to seek help and support for their IPV-related concerns. IPV evidence extracted from social media platforms...
Intimate partner violence (IPV) survivors increasingly use social media platforms to share their experiences and to seek help and support for their IPV-related concerns. IPV evidence extracted from social media platforms can provide valuable information and complement data obtained from conventional data sources (e.g., self-reports and interviews) thereby enhancing our understanding of IPV victimization. This study addressed three research questions: (1) What range of IPV behaviors emerge through qualitative coding? (2) To what extent do machine learning (ML) based text classifications yield results comparable to qualitative coding of IPV behaviors? and (3) Do the conceptualizations that emerge from unsupervised ML capture additional behaviors or contextual information not identified through qualitative analyses? We analyzed 400 posts from women on IPV-related online forums using qualitative content analysis and two ML approaches: supervised text classification and unsupervised topic modeling (Latent Dirichlet Allocation). Supervised learning approaches, notably Random Forest and Neural Networks, proved effective in classifying IPV violence subtypes with high accuracy (F1 scores .62 - .85). A comparison of findings from the qualitative and topic modeling approaches supported the presence of distinct characteristics of IPV: physical and sexual violence, psychological/emotional abuse, and coercive control. The ML model revealed vocabulary patterns consistent with relational and child-related contexts, temporal and frequency indicators of violence, references to legal system engagement, and spatial contexts, elements that were less captured through thematic qualitative coding alone. The consistency of findings across qualitative and ML approaches points to the potential of leveraging ML techniques when analyzing qualitative data, thus enabling the development of timely and effective IPV interventions.
Effects of the ADAPT Military Parenting Program on Parenting Behaviors in a Subsample of Deployed Mothers.
Cheng CH, Lee SS, Gewirtz AH
Prev Sci · 2026 Apr · PMID 42081045 · Publisher ↗
The current study examined the effects of the After Deployment, Adaptive Parenting Tools (ADAPT program), a parenting program for military families, in a subsample of deployed mothers from a larger randomized control tri...
The current study examined the effects of the After Deployment, Adaptive Parenting Tools (ADAPT program), a parenting program for military families, in a subsample of deployed mothers from a larger randomized control trial. Multiple regression was used to examine both observed and self-reported parenting outcomes between intervention and control groups at 1-year follow-up. Drawn from a randomized controlled trial with 336 military families with 5-12-year-old children, the current sample included 56 deployed mothers (Mean age = 34.57 years old; 64.3% were married; 87.5% Caucasians). Results indicated that deployed mothers showed improvement in observed positive parenting (β = .31, p = .01, SE = .32, d = .55) but no significant improvement in overall observed parenting, (β = .24, p = .08, SE = .13, d = .43), no significant reductions in observed harsh discipline (β = .19, p = .18, SE = .14, d = .08) and no significant increases in self-report of parental locus of control (β = - .05, p = .49, SE = .08, d = .25). These findings present the first evidence for the effectiveness of a parenting program for deployed mothers with school-aged children. Improvements in positive parenting are consistent with prior findings from the GenPMTO intervention framework. The lack of intervention effects on harsh discipline suggests that future interventions should consider the cultural meanings and functions of discipline within military contexts and how they may spill over into the home. CLINICAL TRIAL REGISTRATION: This study was registered at ClinicalTrials.gov, study NCT03522610 on 02/16/2018.
The Use of Machine Learning to Predict Offline Adolescent e-Cigarette Use: a Proof-of-Concept.
Cristello JV, Bogusz K, Trucco EM
Prev Sci · 2026 May · PMID 42081044 · Full text
Identification of adolescent e-cigarette use could inform prevention and intervention programming and reduce associated consequences. One way to predict those engaging in use is by examining social media profiles and met...
Identification of adolescent e-cigarette use could inform prevention and intervention programming and reduce associated consequences. One way to predict those engaging in use is by examining social media profiles and metrics. Most studies examining substance use content on social media employ self-report or human coding that have methodological limitations. Thus, the current study developed a supervised machine learning algorithm to classify participants into e-cigarette use categories based on Instagram metrics. Participants (n = 67, M = 18.27; 64.2% female, 82.1% Hispanic/Latino[a/x], 91% White) in the study provided their Instagram data downloaded through the app. Instagram metrics (i.e., number of followers, number following, number of liked comments, number of liked posts, number of posts, and number of messages) were extracted and included as input features in the model. Adolescents reported their e-cigarette use on a self-report measure. A classification tree method was used to classify participants as engaging in e-cigarette use or not. Data was partitioned into a training and test set using stratified sampling. All analyses were performed in Python. Three input features (number of followers, number of liked posts, and number of messages) were selected through hyperparameter-optimized feature selection. The final model accurately detected e-cigarette use 71% of the time. Findings indicate that supervised learning can predict adolescent e-cigarette use with accuracy consistent with other clinical populations. This study establishes that universal aspects of social media may be harbingers for policy makers and tech companies to provide targeted support and messaging.
A Mixed-Methods Study of Policymakers' Adoption of AI to Support Use of Research Evidence: Implications for Artificial Intelligence in Prevention Policy.
Crowley DM, Wright J, Winters A … +8 more , Jones D, Pugel J, O'Neill P, Shaw B, Hamel S, Long E, Donovan M, Scott T
Prev Sci · 2026 May · PMID 42069883 · Publisher ↗
Policymakers are increasingly adopting artificial intelligence (AI) tools to support legislative decision-making, yet there is limited empirical understanding of how these technologies are used and the implications for e...
Policymakers are increasingly adopting artificial intelligence (AI) tools to support legislative decision-making, yet there is limited empirical understanding of how these technologies are used and the implications for evidence-based policymaking. General-purpose AI tools, such as large language models (LLMs), present both opportunities for improved efficiency and risks related to misinformation and lack of transparency. This study examines state legislators' use of AI in policymaking and introduces the AIRE Protocol (AI for Informed and Responsible Evidence-use), a structured framework for developing specialized AI tools grounded in validated evidence. We demonstrate the application of the AIRE Protocol through the development of the Results First AI Assistant, designed to enhance policymakers' access to the Results First Clearinghouse. A mixed-methods approach was used. Forty-five US state legislators participated in live interviews to assess AI adoption patterns, perceived benefits, and concerns. The AIRE Protocol guided the rapid prototyping and iterative development of the AI assistant, with input from policymakers, national policy organizations, and technical experts, resulting in tailored evidence based recommendations. While policymakers expressed interest in AI tools for improving access to information under time constraints, they also raised concerns regarding transparency, reliability, and appropriate use. Our findings suggest that AI tools tailored to policymakers' needs-developed using frameworks like AIRE-will facilitate the integration of validated evidence into legislative decision-making while addressing ethical and practical concerns associated with generalized AI solutions.
Measuring Exposure to Gun Violence and Risky Behavior: Psychometric Validation and Analysis of the Gun Violence Exposure (Gun-X) Scale.
Mitchell KJ, Banyard V, Guziewicz PJ … +1 more , Taylor BG
Prev Sci · 2026 Apr · PMID 42062672 · Full text
Gun violence is a critical public health problem in the United States, requiring improved strategies for early identification and prevention. This study introduces and validates the Gun Violence Exposure (Gun-X) Scale, d...
Gun violence is a critical public health problem in the United States, requiring improved strategies for early identification and prevention. This study introduces and validates the Gun Violence Exposure (Gun-X) Scale, designed to assess awareness of gun violence, threats, and risky firearm behaviors within social networks, including in-person and digital contexts. Data were drawn from a nationally representative sample of 5,311 youth and young adults (ages 10-34) and collected from September 2023 to January 2024. Using a multi-method psychometric approach, findings supported a unidimensional structure with good reliability and consistent model fit across training and validation samples. Item Response Theory analyses indicated high discrimination across items and strongest measurement precision at moderate levels of exposure. Convergent validity was supported through associations with violence exposure, peer gun carrying, and neighborhood risk, while discriminant validity was demonstrated with mental health and social support measures. The 10-item Gun-X Scale provides a reliable and generalizable measure of bystander exposure to gun violence. It has applications in research, clinical screening, and prevention efforts, particularly for characterizing exposure patterns and informing tailored, context-sensitive responses. The scale is intended to assess exposure and should not be used as a standalone tool for selecting individuals for intervention roles.
Role of an Intermediary Organization and State Government Partnership to Advance Practice and Policy in Children's Behavioral Health.
Lang JM, Randall K, Kelly A … +10 more , Bracey J, Bridges-Hightower J, Newkirk K, Lu J, Gregory F, Duran T, Geib CF, Bozak S, Mackey J, Vanderploeg JJ
Prev Sci · 2026 Apr · PMID 42056615 · Publisher ↗
Policymakers navigating an increasingly complex and evolving landscape can benefit from establishing intersectoral partnerships with researchers, providers, schools, family advocates, and other stakeholders in children's...
Policymakers navigating an increasingly complex and evolving landscape can benefit from establishing intersectoral partnerships with researchers, providers, schools, family advocates, and other stakeholders in children's behavioral health. Intermediary organizations, which serve as neutral conveners to bridge cross-system improvements to systems, policy, and practice, are relatively new but are increasing in number and offer an efficient way to support government with strengthening partnerships and systems. We describe an evolving 25-year-long partnership between an independent non-profit intermediary organization, the Child Health and Development Institute (CHDI), and state government that has contributed to systems, policy, and practice improvements as well as research on children's behavioral health. Key components of the partnership include the number and diversity of partners, ongoing involvement of family members with lived experience, use of research and data to inform policy and system development, translational communication of research for policymakers, and the agility, efficiency, and strength as a convener of an independent intermediary organization. Case examples of the partnership's efforts to improve practice, policy, and research through dissemination of evidence-based practices, development of a statewide children's behavioral health plan, implementation of a school-based diversion model, and creation of a strategic plan for the behavioral health workforce are provided. Recommendations are made for states to develop and strengthen partnerships to improve the integration of research, policy, and system development.
The Run It Up Intervention: Addressing the Effects of Structural Determinants on Adolescent Identity, Beliefs, and Involvement in Firearm Violence-Formative Research and Intervention Development.
Edberg MC, Evans WD, Wang Y … +11 more , Andrade EL, Sachdev N, Long MW, Manso L, Sciortino M, Wallace M, Tutt J, Battle V, Rioland K, Bryant D, Mack A
Prev Sci · 2026 Apr · PMID 42033598 · Publisher ↗
Firearm violence in Washington, DC, rose from 2020 to 2022, especially in neighborhoods most affected by long-term socioeconomic marginalization as discussed by Josephson (2022). The Run It Up project is a research-based...
Firearm violence in Washington, DC, rose from 2020 to 2022, especially in neighborhoods most affected by long-term socioeconomic marginalization as discussed by Josephson (2022). The Run It Up project is a research-based effort to reduce the role of community structural factors in prioritizing adolescent beliefs about potential life trajectories ("possible selves") that foreground violence. The project is a partnership between the George Washington University Milken Institute School of Public Health and the Washington Highlands community in DC. This paper presents results of formative research, including 10 adolescent focus groups (n = 80) and 17 key informant interviews conducted over 12 months, to inform intervention development. The resulting intervention seeks to change the calculation of possible selves for adolescents by implementing desirable, tangible trajectories that do not involve violence or pro-violence norms, and in turn reduce youth involvement in firearm violence. These alternative trajectories are implemented through community-based training/mentoring in six career pathways (tracks) that offer attributes and gains meaningful to youth (from the formative research). The intervention includes an intervention branding campaign implemented primarily through social media featuring narratives about the tracks and their attributes. Run It Up is being evaluated using a quasi-experimental design with baseline and follow-up surveys in the intervention and comparison communities. The purpose of the research project is to determine whether and how a university-community partnership can develop and promote alternative life trajectories for youth in communities with high levels of violence, and whether these alternatives increase youth resilience and decrease involvement in violence.
How Adherence to an Evidence-Based Targeted Intervention Procedure is Related to Intervention Effectiveness?
Johander E, Laninga-Wijnen L, Graf D … +2 more , Chávez DV, Salmivalli C
Prev Sci · 2026 Apr · PMID 42012778 · Full text
Research suggests that although teachers' targeted interventions can stop bullying, they still fail in about one-fourth of cases. Yet, most studies to date have not considered how targeted interventions were implemented,...
Research suggests that although teachers' targeted interventions can stop bullying, they still fail in about one-fourth of cases. Yet, most studies to date have not considered how targeted interventions were implemented, leaving open the possibility that improper implementation contributed to these failures. To address this gap, we examined the extent to which school personnel implementing the KiVa® antibullying program in Finland adhered to the program-recommended targeted intervention procedure when addressing bullying cases, and whether modifications to the procedure, influenced intervention effectiveness. We further tested the specific effects of two types of modifications - adaptations and omissions - on effectiveness. Data were collected using ecological momentary assessment, with school personnel documenting in a mobile application the steps they took when addressing bullying cases. The sample included 341 cases involving 396 victimized students (53% female, Mage = 12.39 SD = 2.08) and 733 bullying students (13% female, Mage = 12.52 SD = 1.96) from 22 primary and secondary schools. The results indicated that adherence to procedure varied considerably across intervention steps, and adherence to the full procedure was low. Interventions were, however, more effective when school personnel adhered to the procedure than when they made modifications. Moreover, interventions were least effective, when steps were omitted, whereas adaptations did not significantly reduce effectiveness compared to full adherence, though the trend was in the same direction as with omissions. These findings suggest that closer adherence to evidence-based procedures tends to lead to better outcomes in targeted bullying interventions.
Leveraging State-Level Partnerships to Scale-Up Positive Behavioral Interventions and Supports in U.S. Schools.
Kittelman A, Lewis TJ, Goodman S … +1 more , Powers L
Prev Sci · 2026 Apr · PMID 42008098 · Publisher ↗
Positive Behavioral Interventions and Supports (PBIS) is widely implemented in districts and schools across the United States. State leadership teams play critical roles in facilitating the successful scale up of PBIS in...
Positive Behavioral Interventions and Supports (PBIS) is widely implemented in districts and schools across the United States. State leadership teams play critical roles in facilitating the successful scale up of PBIS in partnership with the National Technical Assistance Center on PBIS. The purpose of this paper is to highlight exemplary partnerships between state leadership teams and the Center on PBIS that have resulted in meaningful and socially significant improvements in school and student outcomes. In addition, given the current climate and shifting federal priorities, we also offer recommendations for state leadership teams focused on continuing to scale and sustain PBIS in schools. These recommendations come from structured interviews with 12 state leadership team members as well as our collective experience supporting state leadership teams for over 25 years.
Designing Rosie the Chatbot with and for Pregnant and New Mothers of Color: a Community-Engaged Study Leveraging Artificial Intelligence and Prevention Science to Improve Maternal and Child Health Outcomes.
Norell EM, Doig AC, Jasczynski M … +6 more , Hunter AS, Gutierrez FXM, Mane H, Mane S, Yue X, Nguyen QC
Prev Sci · 2026 Apr · PMID 41995996 · Full text
Maternal and child health is widely recognized as a marker for a healthy society. According to the Centers for Disease Control and Prevention, the US maternal mortality rates have remained high, with over 1200 maternal d...
Maternal and child health is widely recognized as a marker for a healthy society. According to the Centers for Disease Control and Prevention, the US maternal mortality rates have remained high, with over 1200 maternal deaths occurring in 2021. Recognizing that systemic racism is embedded at all levels of the public health and medical fields, intervention is needed at all levels of the socio-ecological model. Developing health communication tools for pregnant women and new parents is one leverage point in the numerous changes that must occur across public health and medical fields to achieve maternal and child equity. The current study employed focus groups to inform development of an interactive question-and-answer chatbot called Rosie. Participants (n = 30) were all pregnant and new mothers of color residing in the United States. Data were collected in virtual focus groups (N = 6) and transcribed verbatim. Template analysis of focus group transcripts produced three themes in women's health information needs and preferences: (1) Pregnancy and New Parenthood Challenges, (2) Sources of Information and Support, and (3) Chatbot Design. Chatbots as a purveyor of health education information were perceived as a promising approach among our pregnant and new mothers of color participants, who had an array of needs that could be addressed by an intervention such as a chatbot. This technology has broad applicability in the health sphere and may serve as an important supplement to both clinical care and existing early childhood intervention services.
The Impact of Nurse Home-Visiting for Pregnant and Parenting Individuals with Previous Live Births.
Tung GJ, Williams VN, Knudtson MD … +5 more , Franco-Rowe C, Mosley B, Aristides C, Olds DL, Allison MA
Prev Sci · 2026 Apr · PMID 41984290 · Publisher ↗
The Nurse-Family Partnership (NFP) benefits first-time parents and their children; however, its effectiveness for families with previous children (multiparous) is not known. This quasi-experimental study used electronic...
The Nurse-Family Partnership (NFP) benefits first-time parents and their children; however, its effectiveness for families with previous children (multiparous) is not known. This quasi-experimental study used electronic health record data from three NFP sites affiliated with large health systems to evaluate the impact of NFP on pregnancy, birth, and health care utilization outcomes among multiparous families compared with propensity score-matched Medicaid-insured families who did not receive NFP. The study population included 639 multiparous pregnant individuals enrolled in NFP between 2017 and 2021 and a matched comparison group of 6243 non-NFP Medicaid-insured individuals. No statistical differences were found between groups in preterm birth (OR 1.02, 95% CI [0.99, 1.05], p = 0.166), birth weight > 2500 g (OR 1.02, 95% CI [1.00, 1.05], p = 0.094), gestational hypertension (OR 1.02, 95% CI [1.00, 1.05], p = 0.084), child emergency department visits (OR 1.03, 95% CI [0.97, 1.09], p = 0.329), or hospitalizations for injuries (OR 0.0997, 95% CI [0.99, 1.00], p = 0.0503). NICU length of stay was lower for NFP participants (mean difference - 2.45 days) but did not reach statistical significance (95% CI [- 4.91, 0.02], p = 0.052). NFP participants also had higher odds of receiving long-acting reversible contraception (LARC) (OR 1.04, 95% CI [1.01, 1.08], p = 0.019) though this was not significant after adjusting for multiple comparisons. NFP participants did have significantly higher odds of receiving a postpartum visit within 6 weeks (OR 1.22, 95% CI [1.16, 1.24], p < 0.001) and recommended well-child visits (percentage point increase 6.61, 95% CI [3.36, 9.59], p = 0.001). NFP participation among multiparous families was associated with some health care utilization outcomes. These findings suggest a potential mechanism by which NFP may contribute to long-term maternal and child health improvements and highlight the need for further research to assess its effectiveness in this population.
The R-CITY Youth Violence Preventive Intervention: Primary Outcomes from a School Cluster Randomized Controlled Trial.
Bottiani JH, Franco MP, Francis MK … +4 more , Somerville K, Kaihoi CA, Pas ET, Bradshaw CP
Prev Sci · 2026 Apr · PMID 41981234 · Publisher ↗
Student experiences of racism and discrimination in schools can undermine their sense of safety and psychological wellbeing and contribute to aggression and violence. Yet educational systems rarely implement violence pre...
Student experiences of racism and discrimination in schools can undermine their sense of safety and psychological wellbeing and contribute to aggression and violence. Yet educational systems rarely implement violence prevention programming with bias prevention or equity promotion components. To address this gap, researchers and educators partnered to develop R-CITY (Reducing Racism and Violence through Collaborative Intervention with Teachers and Youth; Bottiani et al., School Mental Health, 16(3), 632-648, 2024). A school-level randomized controlled trial was conducted in 27 elementary and middle schools to assess the 'value-added' benefits of supplementing the standard Second Step SEL program (22-27 lessons and group implementation support; comparison condition) with R-CITY's equity-focused one-to-one teacher coaching and grade-differentiated sets of six equity lessons with implementation supports (Second Step + R-CITY, intervention condition). Augmenting Second Step with R-CITY equity-focused components was associated with significant effects on one of six observational measures of student behavior (physical aggression) and one of three teacher-report measures (general teaching self-efficacy), both in the hypothesized direction. Sensitivity analyses excluding the most severely COVID-impacted cohort identified an additional effect on teacher-reported racial discomfort. No significant effects were found on observed teacher practice outcomes or suspension disproportionality rates. Results provide initial evidence that supplementing traditional SEL programming with equity content and coaching can produce significant incremental effects on select outcomes, including reductions in physical aggression and improvements in teacher capacity; however, further research is needed to evaluate the intervention's cost-effectiveness and effects on equity-specific outcomes.
Sexual Health and Relationship Education Needs of People in Recovery for an Opioid Use Disorder.
Wilkerson JM, Gallardo KR, Zoschke IN … +5 more , Stewart HLN, Rodriguez SA, Anosike MU, Pullin J, McCurdy SA
Prev Sci · 2026 Apr · PMID 41974976 · Full text
People in recovery with histories of sex and drug-linked behavior (SDB) have an increased risk of substance use recurrence. However, sexual health concerns remain largely unaddressed by recovery support services research...
People in recovery with histories of sex and drug-linked behavior (SDB) have an increased risk of substance use recurrence. However, sexual health concerns remain largely unaddressed by recovery support services researchers and practitioners. The purpose of this analysis was to describe the sex and relationship concerns of people in recovery for an opioid use disorder living in level II and level III recovery homes. Recovery homes are sober living homes that the National Association for Recovery Residences classifies into four levels based on staffing and service provision. We interviewed 93 residents and thematically analyzed 92 of the resulting transcripts; one was excluded because the participant did not talk about SDB. Most participants avoided sexual experiences or romantic relationships while living in recovery homes. Memories of SDB can trigger unwanted substance use recurrence, and histories of sexual trauma or reliance on drugs during sex impede connection with potential sex or romantic partners. Participants wanted to heal and prepare for healthy sexual or romantic relationships. Recovery residents could benefit from sexual health education that provides the skills for healthy sexual or romantic relationships.
Discrimination, Racism, and Structural Determinants in Youth Violence Prevention Programs: A Scoping Review and Intervention Component Analysis.
Weise M, Manso LM, Chandarana S … +1 more , Edberg M
Prev Sci · 2026 Apr · PMID 41973184 · Publisher ↗
This scoping review maps youth violence prevention studies (1990-2025) that explicitly incorporate discrimination, racism, or structural terms in titles/abstracts, categorizes violence outcomes, and analyzes intervention...
This scoping review maps youth violence prevention studies (1990-2025) that explicitly incorporate discrimination, racism, or structural terms in titles/abstracts, categorizes violence outcomes, and analyzes interventions with components specifically designed to address these factors. From 1034 unique records across a multi-disciplinary group of eight databases, a very small fraction of eligible studies that explicitly centered and named discrimination-related concepts was identified (n = 14). Among these, the use of "discrimination" and "structural" terminology was most salient. Violence outcomes were inconsistently operationalized but most included subjective measures of youth problem behavior or aggression; objective outcome measures related to formalized offenses were moderately used. Five studies featured programs targeting discrimination, racism, or structural determinants and qualified for intervention component analysis (ICA). Most of the studies leveraged multicomponent interventions, but reporting on change mechanisms varied widely. This review highlights a gap between rhetoric around the role of discrimination and structural factors in youth violence and how interventions are designed, indexed, and evaluated. Our ICA findings suggest practical models for embedding discrimination or structural components in prevention programs. Rather than identifying all programs impacting these factors, this review maps those explicitly focused on them. Findings urge funders, journals, and leaders to prioritize evaluations which clearly articulate how prevention interventions address upstream drivers of youth violence.
Area Between Trajectories: Insights into Optimal Group Selection and Trajectory Heterogeneity in Group-Based Trajectory Modeling.
Hsiao YC, Chen CY, Tang MF
Prev Sci · 2026 Mar · PMID 41968190 · Publisher ↗
Group-based trajectory modeling (GBTM) is commonly used to identify longitudinal patterns in health outcomes among older adults, with determining the optimal number of groups being a crucial step. While statistically gro...
Group-based trajectory modeling (GBTM) is commonly used to identify longitudinal patterns in health outcomes among older adults, with determining the optimal number of groups being a crucial step. While statistically grounded criteria are primarily relied upon, clinical relevance is gradually emphasized in medicine to ensure that the identified trajectory heterogeneity appropriately reflects changes in a disease or symptom over time. However, such considerations are often judged through visual comparisons, without concrete approaches for their application. To address this, the area between trajectories (ABT) was introduced as insights for quantifying trajectory group differences. Using a simulated sleep quality dataset, GBTM was applied to build and compare models. Subsequently, ABT was demonstrated to show how it works, while also highlighting its limitations and potential applications.
About
- Frequency
- Sun
- Papers found
- 200
- RSS feed
- Subscribe