Administration and Policy in Mental Health and Mental Health Services Research最新文献

The Brief Jail Mental Health Screen (BJMHS) is one of the most well-known and frequently used tools to conduct routine mental health screening at jail intake. In prior research, the BJMHS results typically have been evaluated overall (i.e., yes/no positive screen). However, there is heterogeneity in symptom presentation and treatment histories among people with serious mental illness, and there are potential consequences of this heterogeneity for mental health administration and policy in jails. We conducted a latent class analysis of BJMHS item-level results using administrative data for 37,998 people booked into a southeastern, metropolitan, U.S. county jail over a 3.5-year period. A 4-class solution provided the best fitting and most interpretable model. The largest class (89.5%) comprised people unlikely to report symptoms or treatment histories (limited symptoms). The next class comprised people who were unlikely to report ongoing symptoms but reported medication and hospitalization (managed symptoms). The third class (2.5%) included people likely to report feeling useless/sinful, prior hospitalization, and current psychiatric medication (depressive symptoms). The fourth class (1.0%) comprised people likely to report thought control, paranoia, feeling useless/sinful, medication, and hospitalization (psychotic symptoms). Controlling for sociodemographic and booking characteristics, people in the managed, depressive, and psychotic symptoms classes had significantly longer jail stays compared to those in the limited symptoms class. People in the managed and depressive symptoms classes were at heightened risk of re-arrest compared to the limited symptoms class. Findings can inform case prioritization and the allocation of resources to support efficient and effective jail-based mental health services.

Ecological momentary assessment (EMA) allows measuring intra-individual processes moment by moment, identifying and modeling, in a naturalistic way, individual levels and changes in different psychological processes. However, active EMA requires a high degree of adherence, as it implies a significant burden for patients. Moreover, there is still no consensus on standardized procedures for implementation. There have been few results in detecting desirable characteristics for the design and implementation of an EMA device. Studies that address these issues from the perspectives of participants in psychotherapeutic processes are needed. To analyze the perspectives of patients, therapists and supervisors on the implementation of an EMA device in a psychotherapeutic treatment for depression. The sample will include eight patients, eleven therapists and five supervisors, taken from a research project that implemented an EMA system for monitoring the dynamics of affectivity at the beginning of psychotherapies for depression. Semi-structured interviews specific to each group are being conducted and analyzed from a qualitative approach based on consensual qualitative research (CQR). Participants reported having a positive evaluation of the study's informational resources and implementation. Difficulties were expressed in responding in the morning hours and the importance of having a customized EMA that is tailored to the needs of the patients was expressed. Furthermore, patients and therapists agreed that the impact of the use of the monitoring system on treatment was neutral or positive. In contrast, patients considered the EMA to be positive for their daily life.

Many training initiatives are underway to increase implementation of evidence-based practice (EBPs) in mental healthcare. However, little is known about what types of trainings and supports yield the highest reach and engagement. Supported by a tax-funded, countywide initiative to improve access to quality care for youths, the current mixed methods study evaluates mental health (MH) provider reach, or registering for the training initiative, and engagement, or participation in training activities, for several EBP training and implementation supports. MH providers were offered free 1) formal EBP workshops, 2) a biweekly learning community, 3) individual case consultation, and 4) confidential online clinical feedback system. To register, interested providers (N = 698) completed a web-based assessment measuring clinical practice information, organizational implementation climate, and EBP knowledge, attitudes, and practices. Thirteen providers, selected via purposeful sampling stratified by level of participation, completed semi-structured qualitative interviews. While the training initiative achieved high reach (66% of county agencies had a provider register), far fewer providers engaged substantially in training. Quantitative results indicated that providers whose professional discipline was not psychology, had higher baseline EBP knowledge, more extensive use of common evidence-based strategies, and less extensive use of other therapy strategies, engaged in more training. Rapid qualitative analysis of interviews expanded upon these findings and illuminated provider, organizational, system, practical, and training activity-specific barriers and facilitators to engagement. Findings suggest the importance of identifying strategies for improving provider engagement in training activities beyond workshops. Implications for future research and training initiatives are discussed.

We aim to use topic modeling, an approach for discovering clusters of related words ("topics"), to predict symptom severity and therapeutic alliance in psychotherapy transcripts, while also identifying the most important topics and overarching themes for prediction. We analyzed 552 psychotherapy transcripts from 124 patients. Using BERTopic (Grootendorst, 2022), we extracted 250 topics each for patient and therapist speech. These topics were used to predict symptom severity and alliance with various competing machine-learning methods. Sensitivity analyses were calculated for a model based on 50 topics, LDA-based topic modeling, and a bigram model. Additionally, we grouped topics into themes using qualitative analysis and identified key topics and themes with eXplainable Artificial Intelligence (XAI). Symptom severity could be predicted with highest accuracy by patient topics ( $r$ =0.45, 95%-CI 0.40, 0.51), whereas alliance was better predicted by therapist topics ( $r$ =0.20, 95%-CI 0.16, 0.24). Drivers for symptom severity were themes related to health and negative experiences. Lower alliance was correlated with various themes, especially psychotherapy framework, income, and everyday life. This analysis shows the potential of using topic modeling in psychotherapy research allowing to predict several treatment-relevant metrics with reasonable accuracy. Further, the use of XAI allows for an analysis of the individual predictive value of topics and themes. Limitations entail heterogeneity across different topic modeling hyperparameters and a relatively small sample size.

The social context of a person, meaning their social relationships and daily social interactions, is an important factor for understanding their mental health. However, personalised feedback approaches to psychotherapy do not consider this factor sufficiently yet. Therefore, we developed an interactive feedback prototype focusing specifically on a person's social relationships as captured with personal social networks (PSN) and daily social interactions as captured with experience sampling methodology (ESM). We describe the development of the prototype as well as two evaluation studies: Semi-structured interviews with students (N = 23) and a focus group discussion with five psychotherapy patients. Participants from both studies considered the prototype useful. The students considered participation in our study, which included social context assessment via PSN and ESM as well as a feedback session, insightful. However, it remains unclear how much insight the feedback procedure generated for the students beyond the insights they already gained from the assessments. The focus group patients indicated that in a clinical context, (social context) feedback may be especially useful to generate insight for the clinician and facilitate collaboration between patient and clinician. Furthermore, it became clear that the current feedback prototype requires explanations by a researcher or trained clinician and cannot function as a stand-alone intervention. As such, we discuss our feedback prototype as a starting point for future research and clinical implementation.

Social interactions are essential for well-being. Therefore, researchers increasingly attempt to capture an individual's social context to predict well-being, including mood. Different tools are used to measure various aspects of the social context. Digital phenotyping is a commonly used technology to assess a person's social behavior objectively. The experience sampling method (ESM) can capture the subjective perception of specific interactions. Lastly, egocentric networks are often used to measure specific relationship characteristics. These different methods capture different aspects of the social context over different time scales that are related to well-being, and combining them may be necessary to improve the prediction of well-being. Yet, they have rarely been combined in previous research. To address this gap, our study investigates the predictive accuracy of mood based on the social context. We collected intensive within-person data from multiple passive and self-report sources over a 28-day period in a student sample (Participants: N = 11, ESM measures: N = 1313). We trained individualized random forest machine learning models, using different predictors included in each model summarized over different time scales. Our findings revealed that even when combining social interactions data using different methods, predictive accuracy of mood remained low. The average coefficient of determination over all participants was 0.06 for positive and negative affect and ranged from - 0.08 to 0.3, indicating a large amount of variance across people. Furthermore, the optimal set of predictors varied across participants; however, predicting mood using all predictors generally yielded the best predictions. While combining different predictors improved predictive accuracy of mood for most participants, our study highlights the need for further work using larger and more diverse samples to enhance the clinical utility of these predictive modeling approaches.

Ecological Momentary Assessment (EMA) is a data collection approach utilizing smartphone applications or wearable devices to gather insights into daily life. EMA has advantages over traditional surveys, such as increasing ecological validity. However, especially prolonged data collection can burden participants by disrupting their everyday activities. Consequently, EMA studies can have comparably high rates of missing data and face problems of compliance. Giving participants access to their data via accessible feedback reports, as seen in citizen science initiatives, may increase participant motivation. Existing frameworks to generate such reports focus on single individuals in clinical settings and do not scale well to large datasets. Here, we introduce FRED (Feedback Reports on EMA Data) to tackle the challenge of providing personalized reports to many participants. FRED is an interactive online tool in which participants can explore their own personalized data reports. We showcase FRED using data from the WARN-D study, where 867 participants were queried for 85 consecutive days with four daily and one weekly survey, resulting in up to 352 observations per participant. FRED includes descriptive statistics, time-series visualizations, and network analyses on selected EMA variables. Participants can access the reports online as part of a Shiny app, developed via the R programming language. We make the code and infrastructure of FRED available in the hope that it will be useful for both research and clinical settings, given that it can be flexibly adapted to the needs of other projects with the goal of generating personalized data reports.

Having a mental health (MH) or substance use (SU) issue can make the transition from prison to the community a challenging process. Despite this, few studies have quantified how justice-involved individuals with mental health issues only, substance use only, those with both struggles, and those with neither are uniquely affected. Using a sample of re-entering men who were released from twelve state prisons in the United States, we assessed the effects of having MH and SU issues on their drug use during re-entry. Furthermore, we examined their differing coping reactions to housing insecurity, joblessness, and family tension after release. The results demonstrated that respondents' risk of SU during re-entry was associated with MH and SU issues measured at release. Those with co-occurring MH and SU challenges were at the highest risk of SU during re-entry. Furthermore, challenging life situations during re-entry exerted an amplified effect on SU for respondents with both anxiety and SU issues. The findings suggest that post-incarcerated individuals with co-occurring MH and SU issues have the highest risk of SU, and their reaction to re-entry barriers is distinct from their peers. Quality services to address co-occurring MH and SU may be needed to facilitate a smooth transition from prison to the community.