Mesoporous Gold: Substrate-Dependent Growth Dynamics, Strain Accumulation, and Electrocatalytic Activity for Biosensing.

Understanding the growth of mesoporous crystalline materials, such as mesoporous metals, on different substrates can provide valuable insights into the crystal growth dynamics and the redox reactions that influence their electrochemical sensing performance. Herein, it is demonstrated how the amorphous nature of the glass substrate can suppress the typical <111> oriented growth in mesoporous Au (mAu) films. The suppressed <111> growth is manifested as an accumulation of strain, leading to the generation of abundant surface defects, which are beneficial for enhancing the electrochemical activity. The fine structuring attained enables dramatically accelerated diffusion and enhances the electrochemical sensing performance for disease-specific biomolecules. As a proof-of-concept, the as-fabricated glass-grown mAu film demonstrates high sensitivity in electrochemical detection of SARS-CoV-2-specific RNA with a limit of detection (LoD) as low as 1 attomolar (aM).

The Impact of Sparse Datasets When Harmonizing Data from Studies with Different Measures of the Same Construct.

Prevention science has increasingly turned to integrative data analysis (IDA) to combine individual participant-level data from multiple studies of the same topic, allowing us to evaluate overall effect size, test and model heterogeneity, and examine mediation. Studies included in IDA often use different measures for the same construct, leading to sparse datasets. We introduce a graph theory method for summarizing patterns of sparseness and use simulations to explore the impact of different patterns on measurement bias within three different measurement models: a single common factor, a hierarchical model, and a bifactor model. We simulated 1000 datasets with varying levels of sparseness and used Bayesian methods to estimate model parameters and evaluate bias. Results clarified that bias due to sparseness will depend on the strength of the general factor, the measurement model employed, and the level of indirect linkage among measures. We provide an example using a synthesis dataset that combined data on youth depression from 4146 youth who participated in 16 randomized field trials of prevention programs. Given that different synthesis datasets will embody different patterns of sparseness, we conclude by recommending that investigators use simulation methods to explore the potential for bias given the sparseness patterns they encounter.

Drone-Robot to Clean Power Line Insulators.

The inspection and maintenance of transmission systems are necessary for their proper functioning. In this way, among the line's critical points are the insulator chains, which are responsible for providing insulation between conductors and structures. The accumulation of pollutants on the insulator surface can cause failures in the power system, leading to power supply interruptions. Currently, the cleaning of insulator chains is performed manually by operators who climb towers and use cloths, high-pressure washers, or even helicopters. The use of robots and drones is also under study, presenting challenges to be overcome. This paper presents the development of a drone-robot for cleaning insulator chains. The drone-robot was designed to identify insulators by camera and perform cleaning through a robotic module. This module is attached to the drone and carries a battery-powered portable washer, a reservoir for demineralized water, a depth camera, and an electronic control system. This paper includes a literature review on the state of the art related to strategies used for cleaning insulator chains. Based on this review, the justification for the construction of the proposed system is presented. The methodology used in the development of the drone-robot is then described. The system was validated in a controlled environment and in field experimental tests, with the ensuing discussions and conclusions formulated, along with suggestions for future work.

Accounting for Context in Randomized Trials after Assignment.

Many preventive trials randomize individuals to intervention condition which is then delivered in a group setting. Other trials randomize higher levels, say organizations, and then use learning collaboratives comprised of multiple organizations to support improved implementation or sustainment. Other trials randomize or expand existing social networks and use key opinion leaders to deliver interventions through these networks. We use the term contextually driven to refer generally to such trials (traditionally referred to as clustering, where groups are formed either pre-randomization or post-randomization - i.e., a cluster-randomized trial), as these groupings or networks provide fixed or time-varying contexts that matter both theoretically and practically in the delivery of interventions. While such contextually driven trials can provide efficient and effective ways to deliver and evaluate prevention programs, they all require analytical procedures that take appropriate account of non-independence, something not always appreciated. Published analyses of many prevention trials have failed to take this into account. We discuss different types of contextually driven designs and then show that even small amounts of non-independence can inflate actual Type I error rates. This inflation leads to rejecting the null hypotheses too often, and erroneously leading us to conclude that there are significant differences between interventions when they do not exist. We describe a procedure to account for non-independence in the important case of a two-arm trial that randomizes units of individuals or organizations in both arms and then provides the active treatment in one arm through groups formed after assignment. We provide sample code in multiple programming languages to guide the analyst, distinguish diverse contextually driven designs, and summarize implications for multiple audiences.

Landscape of HIV Implementation Research Funded by the National Institutes of Health: A Mapping Review of Project Abstracts.

In 2019, the requisite biomedical and behavioral interventions to eliminate new HIV infections exist. "Ending the HIV Epidemic" now becomes primarily a challenge of will and implementation. This review maps the extent to which implementation research (IR) has been integrated into HIV research by reviewing the recent funding portfolio of the NIH. We searched NIH RePORTER for HIV and IR-related research projects funded from January 2013 to March 2018. The 4629 unique studies identified were screened using machine learning and manual methods. 216 abstracts met the eligibility criteria of HIV and IR. Key study characteristics were then abstracted. NIH currently funds HIV studies that are either formally IR (n = 109) or preparatory for IR (n = 107). Few (13%) projects mentioned a guiding implementation model, theory, or framework, and only 56% of all studies explicitly mentioned measuring an implementation outcome. Considering the study aims along an IR continuum, 18 (8%) studies examined barriers and facilitators, 43 (20%) developed implementation strategies, 46 (21%) piloted strategies, 73 (34%) tested a single strategy, and 35 (16%) compared strategies. A higher proportion of formal IR projects involved established interventions (e.g., integrated services) compared to newer interventions (e.g., pre-exposure prophylaxis). Prioritizing HIV-related IR in NIH and other federal funding opportunity announcements and expanded training in implementation science could have a substantial impact on ending the HIV pandemic. This review serves as a baseline by which to compare funding patterns and the sophistication of IR in HIV research over time.

Design Considerations for Implementing eHealth Behavioral Interventions for HIV Prevention in Evolving Sociotechnical Landscapes.

PURPOSE OF REVIEW: Despite tremendous potential for public health impact and continued investments in development and evaluation, it is rare for eHealth behavioral interventions to be implemented broadly in practice. Intervention developers may not be planning for implementation when designing technology-enabled interventions, thus creating greater challenges for real-world deployment following a research trial. To facilitate faster translation to practice, we aimed to provide researchers and developers with an implementation-focused approach and set of design considerations as they develop new eHealth programs. RECENT FINDINGS: Using the Accelerated Creation-to-Sustainment model as a lens, we examined challenges and successes experienced during the development and evaluation of four diverse eHealth HIV prevention programs for young men who have sex with men: Keep It Up!, Harnessing Online Peer Education, Guy2Guy, and HealthMindr. HIV is useful for studying eHealth implementation because of the substantial proliferation of diverse eHealth interventions with strong evidence of reach and efficacy and the responsiveness to rapid and radical disruptions in the field. Rather than locked-down products to be disseminated, eHealth interventions are complex sociotechnical systems that require continual optimization, vigilance to monitor and troubleshoot technological issues, and decision rules to refresh content and functionality while maintaining fidelity to core intervention principles. Platform choice and sociotechnical relationships (among end users, implementers, and the technology) heavily influence implementation needs and challenges. We present a checklist of critical implementation questions to address during intervention development. In the absence of a clear path forward for eHealth implementation, deliberate design of an eHealth intervention's service and technological components in tandem with their implementation plans is critical to mitigating barriers to widespread use. The design considerations presented can be used by developers, evaluators, reviewers, and funders to prioritize the pragmatic scalability of eHealth interventions in research.

Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles From a Pilot Program at Crisis Text Line.

Data sharing between technology companies and academic health researchers has multiple health care, scientific, social, and business benefits. Many companies remain wary about such sharing because of unaddressed concerns about ethics, data security, logistics, and public relations. Without guidance on these issues, few companies are willing to take on the potential work and risks involved in noncommercial data sharing, and the scientific and societal potential of their data goes unrealized. In this paper, we describe the 18-month long pilot of a data-sharing program led by Crisis Text Line (CTL), a not-for-profit technology company that provides a free 24/7 text line for people in crisis. The primary goal of the data-sharing pilot was to design, develop, and implement a rigorous framework of principles and protocols for the safe and ethical sharing of user data. CTL used a stakeholder-based policy process to develop a feasible and ethical data-sharing program. The process comprised forming a data ethics committee; identifying policy challenges and solutions; announcing the program and generating interest; and revising the policy and launching the program. Once the pilot was complete, CTL examined how well the program ran and compared it with other potential program models before putting in place the program that was most suitable for its organizational needs. By drawing on CTL's experiences, we have created a 3-step set of guidelines for other organizations that wish to develop their own data-sharing program with academic researchers. The guidelines explain how to (1) determine the value and suitability of the data and organization for creating a data-sharing program; (2) decide on an appropriate data sharing and collaboration model; and (3) develop protocols and technical solutions for safe and ethical data sharing and the best organizational structure for implementing the program. An internal evaluation determined that the pilot satisfied CTL's goals of sharing scientific data and protecting client confidentiality. The policy development process also yielded key principles and protocols regarding the ethical challenges involved in data sharing that can be applied by other organizations. Finally, CTL's internal review of the pilot program developed a number of alternative models for sharing data that will suit a range of organizations with different priorities and capabilities. In implementing and studying this pilot program, CTL aimed both to optimize its own future data-sharing programs and to inform similar decisions made by others. Open data programs are both important and feasible to establish. With careful planning and appropriate resources, data sharing between big data companies and academic researchers can advance their shared mission to benefit society and improve lives.

Redesigning Implementation Measurement for Monitoring and Quality Improvement in Community Delivery Settings.

The field of prevention has established the potential to promote child adjustment across a wide array of outcomes. However, when evidence-based prevention programs have been delivered at scale in community settings, declines in implementation and outcomes have resulted. Maintaining high quality implementation is a critical challenge for the field. We describe steps towards the development of a practical system to monitor and support the high-quality implementation of evidence-based prevention programs in community settings. Research on the implementation of an evidence-based parenting program for divorcing families called the "New Beginnings Program" serves as an illustration of the promise of such a system. As a first step, we describe a multidimensional theoretical model of implementation that links aspects of program delivery with improvements in participant outcomes. We then describe research on the measurement of each of these implementation dimensions and test their relations to intended program outcomes. As a third step, we develop approaches to the assessment of these implementation constructs that are feasible to use in community settings and to establish their reliability and validity. We focus on the application of machine learning algorithms and web-based data collection systems to assess implementation and provide support for high quality delivery and positive outcomes. Examples are presented to demonstrate that valid and reliable measures can be collected using these methods. Finally, we envision how these measures can be used to develop an unobtrusive system to monitor implementation and provide feedback and support in real time to maintain high quality implementation and program outcomes.

The Cascading Effects of Multiple Dimensions of Implementation on Program Outcomes: a Test of a Theoretical Model.

This study tests a theoretical cascade model in which multiple dimensions of facilitator delivery predict indicators of participant responsiveness, which in turn lead to improvements in targeted program outcomes. An effectiveness trial of the 10-session New Beginnings Program for divorcing families was implemented in partnership with four county-level family courts. This study included 366 families assigned to the intervention condition who attended at least one session. Independent observers provided ratings of program delivery (i.e., fidelity to the curriculum and process quality). Facilitators reported on parent attendance and parents' competence in home practice of program skills. At pretest and posttest, children reported on parenting and parents reported child mental health. We hypothesized effects of quality on attendance, fidelity and attendance on home practice, and home practice on improvements in parenting and child mental health. Structural Equation Modeling with mediation and moderation analyses were used to test these associations. Results indicated quality was significantly associated with attendance, and attendance moderated the effect of fidelity on home practice. Home practice was a significant mediator of the links between fidelity and improvements in parent-child relationship quality and child externalizing and internalizing problems. Findings provide support for fidelity to the curriculum, process quality, attendance, and home practice as valid predictors of program outcomes for mothers and fathers. Future directions for assessing implementation in community settings are discussed.

"Home Practice Is the Program": Parents' Practice of Program Skills as Predictors of Outcomes in the New Beginnings Program Effectiveness Trial.

An examination of the content and processes of evidence-based programs is critical for empirically evaluating theories about how programs work, the "action theory" of the program (West et al. in American Journal of Community Psychology, 21, 571-605, 1993). The New Beginnings Program (NBP; Wolchik et al., 2007), a parenting-after-divorce preventive intervention, theorizes that program-induced improvements in parenting across three domains: positive relationship quality, effective discipline, and protecting children from interparental conflict, will reduce the negative outcomes that are common among children from divorced families. The process theory is that home practice of program skills related to these parenting domains is the primary mechanism leading to positive change in parenting. This theory was tested using multi-rater data from 477 parents in the intervention condition of an effectiveness trial of the NBP (Sandler et al. 2016a, 2016b). Four research questions were addressed: Does home practice of skills predict change in the associated parenting outcomes targeted by the program? Is the effect above and beyond the influence of attendance at program sessions? What indicators of home practice (i.e., attempts, fidelity, efficacy, and competence) are most predictive of improvements in parenting? Do these indicators predict parenting improvements in underserved subpopulations (i.e., fathers and Latinos)? Structural Equation Modeling analyses indicated that parent-reported efficacy and provider-rated parent competence of home practice predicted improvements in the targeted parenting domains according to both parent and child reports. Moreover, indicators of home practice predicted improvements in parenting for fathers and Latinos, although patterns of effects varied by parenting outcome.

Human Subjects Protection and Technology in Prevention Science: Selected Opportunities and Challenges.

Internet-connected devices are changing the way people live, work, and relate to one another. For prevention scientists, technological advances create opportunities to promote the welfare of human subjects and society. The challenge is to obtain the benefits while minimizing risks. In this article, we use the guiding principles for ethical human subjects research and proposed changes to the Common Rule regulations, as a basis for discussing selected opportunities and challenges that new technologies present for prevention science. The benefits of conducting research with new populations, and at new levels of integration into participants' daily lives, are presented along with five challenges along with technological and other solutions to strengthen the protections that we provide: (1) achieving adequate informed consent with procedures that are acceptable to participants in a digital age; (2) balancing opportunities for rapid development and broad reach, with gaining adequate understanding of population needs; (3) integrating data collection and intervention into participants' lives while minimizing intrusiveness and fatigue; (4) setting appropriate expectations for responding to safety and suicide concerns; and (5) safeguarding newly available streams of sensitive data. Our goal is to promote collaboration between prevention scientists, institutional review boards, and community members to safely and ethically harness advancing technologies to strengthen impact of prevention science.

A Statistical Method for Synthesizing Mediation Analyses Using the Product of Coefficient Approach Across Multiple Trials.

Mediation analysis often requires larger sample sizes than main effect analysis to achieve the same statistical power. Combining results across similar trials may be the only practical option for increasing statistical power for mediation analysis in some situations. In this paper, we propose a method to estimate: 1) marginal means for mediation path a, the relation of the independent variable to the mediator; 2) marginal means for path b, the relation of the mediator to the outcome, across multiple trials; and 3) the between-trial level variance-covariance matrix based on a bivariate normal distribution. We present the statistical theory and an R computer program to combine regression coefficients from multiple trials to estimate a combined mediated effect and confidence interval under a random effects model. Values of coefficients a and b, along with their standard errors from each trial are the input for the method. This marginal likelihood based approach with Monte Carlo confidence intervals provides more accurate inference than the standard meta-analytic approach. We discuss computational issues, apply the method to two real-data examples and make recommendations for the use of the method in different settings.

Blending Qualitative and Computational Linguistics Methods for Fidelity Assessment: Experience with the Familias Unidas Preventive Intervention.

Careful fidelity monitoring and feedback are critical to implementing effective interventions. A wide range of procedures exist to assess fidelity; most are derived from observational assessments (Schoenwald and Garland, Psycholog Assess 25:146-156, 2013). However, these fidelity measures are resource intensive for research teams in efficacy/effectiveness trials, and are often unattainable or unmanageable for the host organization to rate when the program is implemented on a large scale. We present a first step towards automated processing of linguistic patterns in fidelity monitoring of a behavioral intervention using an innovative mixed methods approach to fidelity assessment that uses rule-based, computational linguistics to overcome major resource burdens. Data come from an effectiveness trial of the Familias Unidas intervention, an evidence-based, family-centered preventive intervention found to be efficacious in reducing conduct problems, substance use and HIV sexual risk behaviors among Hispanic youth. This computational approach focuses on "joining," which measures the quality of the working alliance of the facilitator with the family. Quantitative assessments of reliability are provided. Kappa scores between a human rater and a machine rater for the new method for measuring joining reached 0.83. Early findings suggest that this approach can reduce the high cost of fidelity measurement and the time delay between fidelity assessment and feedback to facilitators; it also has the potential for improving the quality of intervention fidelity ratings.

Biochemical and biophysical characterization of recombinant rat apolipoprotein E: similarities to human apolipoprotein E3.

Apolipoprotein E (apoE) is an anti-atherogenic protein that plays a critical role in maintaining plasma cholesterol and triglyceride homeostasis by virtue of its ability to act as a ligand for the low-density lipoprotein receptor (LDLr) family of proteins. In this study, we characterized the biochemical and biophysical features of recombinant rat apoE, in comparison with those of human apoE3. Rat apoE was overexpressed in Escherichia coli using a codon optimized system and purified by affinity chromatography. SDS-PAGE and RP-HPLC of rat apoE confirmed the purity, while immunoblot verified the identity and cross-reactivity with the LDLr-binding region of apoE3. The α-helical content was calculated to be ~45% by circular dichroism spectroscopy. The protein exists in a predominantly tetrameric form in lipid-free state. Chemical denaturation studies reveal that the unfolding pattern is biphasic with mid points of denaturation corresponding to 0.8 and 2.2 M guanidine hydrochloride, suggesting the presence of two domains. Rat apoE converts DMPC vesicles to smaller DMPC/apoE complexes with a first order rate constant of 0.12 min(-1). It has the ability to bind the LDLr and to heparin. Our studies indicate that although its sequence resembles apoE4, an isoform of apoE3, rat apoE displays the biophysical behavior of apoE3.

A machine learning approach to improve implementation monitoring of family-based preventive interventions in primary care.

Background: Evidence-based parenting programs effectively prevent the onset and escalation of child and adolescent behavioral health problems. When programs have been taken to scale, declines in the quality of implementation diminish intervention effects. Gold-standard methods of implementation monitoring are cost-prohibitive and impractical in resource-scarce delivery systems. Technological developments using computational linguistics and machine learning offer an opportunity to assess fidelity in a low burden, timely, and comprehensive manner. Methods: In this study, we test two natural language processing (NLP) methods [i.e., Term Frequency-Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers (BERT)] to assess the delivery of the Family Check-Up 4 Health (FCU4Health) program in a type 2 hybrid effectiveness-implementation trial conducted in primary care settings that serve primarily Latino families. We trained and evaluated models using 116 English and 81 Spanish-language transcripts from the 113 families who initiated FCU4Health services. We evaluated the concurrent validity of the TF-IDF and BERT models using observer ratings of program sessions using the COACH measure of competent adherence. Following the Implementation Cascade model, we assessed predictive validity using multiple indicators of parent engagement, which have been demonstrated to predict improvements in parenting and child outcomes. Results: Both TF-IDF and BERT ratings were significantly associated with observer ratings and engagement outcomes. Using mean squared error, results demonstrated improvement over baseline for observer ratings from a range of 0.83-1.02 to 0.62-0.76, resulting in an average improvement of 24%. Similarly, results demonstrated improvement over baseline for parent engagement indicators from a range of 0.81-27.3 to 0.62-19.50, resulting in an approximate average improvement of 18%. Conclusions: These results demonstrate the potential for NLP methods to assess implementation in evidence-based parenting programs delivered at scale. Future directions are presented. Trial registration: NCT03013309 ClinicalTrials.gov.

Research has shown that evidence-based parenting programs effectively prevent the onset and escalation of child and adolescent behavioral health problems. However, if they are not implemented with fidelity, there is a potential that they will not produce the same effects. Gold-standard methods of implementation monitoring include observations of program sessions. This is expensive and difficult to implement in delivery settings with limited resources. Using data from a trial of the Family Check-Up 4 Health program in primary care settings that served Latino families, we investigated the potential to make use of a form of machine learning called natural language processing (NLP) to monitor program delivery. NLP-based ratings were significantly associated with independent observer ratings of fidelity and participant engagement outcomes. These results demonstrate the potential for NLP methods to monitor implementation in evidence-based parenting programs delivered at scale.

Categorising implementation determinants and strategies within the US HIV implementation literature: a systematic review protocol.

INTRODUCTION: Despite decreased rates of new infections, HIV/AIDS continues to impact certain US populations. In order to achieve the goals laid out in the Ending the HIV Epidemic (EHE) in the US initiative, implementation science is needed to expand the sustained use of effective prevention and treatment interventions, particularly among priority populations at risk for and living with HIV/AIDS. Over 200 HIV-related implementation studies have been funded by the US National Institutes of Health. Therefore, a comprehensive review of the literature identifying implementation determinants (barriers and facilitators) and categorising implementation strategies across the continuum of HIV prevention and care in the USA is appropriate and needed to enhance current knowledge and help achieve the goals laid out in the EHE national strategic plan. METHODS AND ANALYSIS: This systematic review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Between November 2020 and January 2022, a broad database search strategy of Ovid MEDLINE, PsycINFO and Web of Science was conducted to capture implementation-related studies along the HIV prevention and care continuum. Articles were eligible for inclusion if they were: conducted in the USA, published after the year 2000, written in English, related to HIV/AIDS, focused on outcomes related to dissemination and implementation (ie, did not test/evaluate/explore implementation determinants or strategies) and were behavioural studies (ie, not basic science). We plan to conduct three systematic reviews to identify and categorise determinants and strategies associated with three HIV focus areas: pre-exposure prophylaxis, testing/diagnosing and linkage to care, and treatment. Determinants will be coded according to an adapted Consolidated Framework for Implementation Research 2.0. Implementation strategies and outcomes will be categorised in accordance with existing taxonomies and frameworks. ETHICS AND DISSEMINATION: Ethics approval is not applicable. No original data will be collected. Results will be disseminated through peer-reviewed publications, conference presentations and via online tools. PROSPERO REGISTRATION NUMBER: CRD42021233089.

Partnerships for the design, conduct, and analysis of effectiveness, and implementation research: experiences of the prevention science and methodology group.

What progress prevention research has made comes through strategic partnerships with communities and institutions that host this research, as well as professional and practice networks that facilitate the diffusion of knowledge about prevention. We discuss partnership issues related to the design, analysis, and implementation of prevention research and especially how rigorous designs, including random assignment, get resolved through a partnership between community stakeholders, institutions, and researchers. These partnerships shape not only study design, but they determine the data that can be collected and how results and new methods are disseminated. We also examine a second type of partnership to improve the implementation of effective prevention programs into practice. We draw on social networks to studying partnership formation and function. The experience of the Prevention Science and Methodology Group, which itself is a networked partnership between scientists and methodologists, is highlighted.

Crisis text-line interventions: Evaluation of texters' perceptions of effectiveness.

OBJECTIVE: Crisis Text Line (CTL), the largest provider of text-based crisis intervention services in the U.S., has answered nearly 7 million conversations since its inception in 2013. The study's objective was to assess texter's perceptions of the effectiveness of CTL crisis interventions. METHOD: Survey data completed by 85,877 texters linked to volunteer crisis counselor (CC) reports from October 12, 2017, to October 11, 2018 were analyzed. The relationship of several effectiveness measures with texters' demographic and psychosocial characteristics, frequency of CTL usage, and texters' perceptions of engagement with their CCs was examined using a series of logistic regression analyses. RESULTS: By the end of the text-based conversation, nearly 90% of suicidal texters reported that the conversation was helpful, and nearly half reported being less suicidal. CONCLUSIONS: Our study offers evidence for CTL's perceived effectiveness. These findings are of critical importance in light of the launch of a nationwide three-digit number (988) for suicide prevention and mental health crisis supports in the U.S., which will include texting.

Individuals who text crisis text line: Key characteristics and opportunities for suicide prevention.

OBJECTIVE: Text-based crisis services are increasingly prominent, with inclusion in the national 988 crisis number launching in 2022. Yet little is known about who uses them. This study seeks to understand the population served by Crisis Text Line (CTL), the largest crisis text service in the United States. METHODS: Secondary data analysis was conducted on de-identified Crisis Counselor reports, texter post-conversation survey responses, and anonymized text conversation data from 85,877 texters who contacted CTL during a 12-month period. We examined Crisis Counselor's ratings of suicide ideation severity, texters' reports of race, gender, sexual orientation, recent mental health symptoms, and additional sources of help, and logs of frequency of contact. RESULTS: 76% of texters were under 25. 79% were female. 48% identified as other than heterosexual/straight. 64% had only one conversation. 79% were above the clinical cutoff for depression and 80% for anxiety, while 23% had thoughts of suicide. 23% received help from a doctor or therapist, and 28% received help only from CTL. CONCLUSIONS: CTL reaches a highly distressed, young, mostly female population, including typically underserved minorities and a substantial percentage of individuals who do not receive help elsewhere. These findings support the decision to include texting in the forthcoming national 988 implementation.

Determinants of Implementation for HIV Pre-exposure Prophylaxis Based on an Updated Consolidated Framework for Implementation Research: A Systematic Review.

BACKGROUND: Delivery and use of HIV pre-exposure prophylaxis (PrEP) are suboptimal in the United States. Previous reviews of barriers and facilitators have not used an implementation science lens, limiting comprehensiveness and the link to implementation strategies. To summarize the state of the science, we systematically reviewed determinants of PrEP implementation using the updated Consolidated Framework for Implementation Research (CFIR 2.0). SETTING: PrEP-eligible communities and delivery settings in the United States. METHODS: In January 2021, we searched Ovid MEDLINE, PsycINFO, and Web of Science for peer-reviewed articles related to HIV/AIDS, interventions, implementation, and determinants or strategies. We identified 286 primary research articles published after 1999 about US-based PrEP implementation. Team members extracted discrete "mentioned" and "measured" determinants, coding each by setting, population, valence, measurement, and CFIR 2.0 construct. RESULTS: We identified 1776 mentioned and 1952 measured determinants from 254 to 239 articles, respectively. Two-thirds of measured determinants were of PrEP use by patients as opposed to delivery by providers. Articles contained few determinants in the inner setting or process domains (ie, related to the delivery context), even among studies of specific settings. Determinants across priority populations also focused on individual patients and providers rather than structural or logistical factors. CONCLUSION: Our findings suggest substantial knowledge in the literature about general patient-level barriers to PrEP use and thus limited need for additional universal studies. Instead, future research should prioritize identifying determinants, especially facilitators, unique to understudied populations and focus on structural and logistical features within current and promising settings (eg, pharmacies) that support integration of PrEP into clinical practice.