Describing suicidality: an investigation of qualitative SSF responses

Given the incidence and seriousness of suicidality in clinical practice, the need for new and better ways to assess suicide risk is clear. While there are many published assessment instruments in the literature, survey data suggest that these measure are not widely used. One possible explanation is that current quantitatively developed assessment instruments may fail to capture something essential about the suicidal patient's experience. The current exploratory study examined a range of open ended qualitative written responses made by suicidal outpatients to five assessment prompts from the Suicide Status Form (SSF)--psychological pain, press, perturbation, hopelessness, and self-hate. Two different samples of suicidal outpatients seeking treatment, including suicidal college students (n = 119) and active duty U.S. Air Force personnel (n = 33), provided a wide range of written responses to the five SSF prompts. A qualitative coding manual was developed through a step-by-step methodology; two naive coders were trained to use the coding system and were able to sort all the patients' written responses into the content categories with very high interrater reliability (Kappa > .80). Certain written qualitative responses of the patients were more frequent than others, both within and across the five SSF constructs. Among a range of specific exploratory findings, one general finding was that two thirds of the 636 obtained written responses could be reliably categorized under four major content headings: relational (22%), role responsibilities (20%), self (15%), and unpleasant internal states (10%). Theoretical, research, and clinical implications of the methodology and data are discussed.     

Cerebral blood flow in 5‐ to 8‐month‐olds: Regional tissue maturity is associated with infant affect

Infancy is marked by rapid neural and emotional development. The relation between brain function and emotion in infancy, however, is not well understood. Methods for measuring brain function predominantly rely on the BOLD signal; however, interpretation of the BOLD signal in infancy is challenging because the neuronal‐hemodynamic relation is immature. Regional cerebral blood flow (rCBF) provides a context for the infant BOLD signal and can yield insight into the developmental maturity of brain regions that may support affective behaviors. This study aims to elucidate the relations among rCBF, age, and emotion in infancy. One hundred and seven mothers reported their infants' (infant age M ± SD = 6.14 ± 0.51 months) temperament. A subsample of infants completed MRI scans, 38 of whom produced usable perfusion MRI during natural sleep to quantify rCBF. Mother‐infant dyads completed the repeated Still‐Face Paradigm, from which infant affect reactivity and recovery to stress were quantified. We tested associations of infant age at scan, temperament factor scores, and observed affect reactivity and recovery with voxel‐wise rCBF. Infant age was positively associated with CBF in nearly all voxels, with peaks located in sensory cortices and the ventral prefrontal cortex, supporting the formulation that rCBF is an indicator of tissue maturity. Temperamental Negative Affect and recovery of positive affect following a stressor were positively associated with rCBF in several cortical and subcortical limbic regions, including the orbitofrontal cortex and inferior frontal gyrus. This finding yields insight into the nature of affective neurodevelopment during infancy. Specifically, infants with relatively increased prefrontal cortex maturity may evidence a disposition toward greater negative affect and negative reactivity in their daily lives yet show better recovery of positive affect following a social stressor.     

Fostering Diversity in the Creative Arts by Addressing Students' Capacity to Aspire

Research on young people's aspirations and their capacity to aspire to higher education has proliferated in recent decades, however, very little attention has been paid to the creative arts. Diversity in the arts remains a persistent issue in many nations, and repeated attempts to promote diversity in the Australian arts community have had limited impact, suggesting the need for new approaches. Drawing on data from a 4‐year longitudinal study of students from ages 8 to 18 (n = 6,492) in government schools, we examine school students' aspirations for careers in the arts. Arts‐related careers were popular among students, yet we found a distinct lack of diversity among those aspiring to such careers. Using logistic regression analysis we found that being female, high achieving, from an English‐speaking background, possessing high cultural capital, and attending advantaged schools were significant predictors of interest in the arts, suggesting the likely reproduction of existing patterns of participation. We argue that initiatives within schools are essential to disrupting these patterns and building the capacity of a more diverse range of students to aspire to careers in the arts.     

The Impact of Item Misspecification and Dichotomization on Class and Parameter Recovery in LCA of Count Data

Mixture analysis of count data has become increasingly popular among researchers of substance use, behavioral analysis, and program evaluation. However, this increase in popularity seems to have occurred along with adoption of some conventions in model specification based on arbitrary heuristics that may impact the validity of results. Findings from a systematic review of recent drug and alcohol publications suggested count variables are often dichotomized or misspecified as continuous normal indicators in mixture analysis. Prior research suggests that misspecifying skewed distributions of continuous indicators in mixture analysis introduces bias, though the consequences of this practice when applied to count indicators has not been studied. The present work describes results from a simulation study examining bias in mixture recovery when count indicators are dichotomized (median split; presence vs. absence), ordinalized, or the distribution is misspecified (continuous normal; incorrect count distribution). All distributional misspecifications and methods of categorizing resulted in greater bias in parameter estimates and recovery of class membership relative to specifying the true distribution, though dichotomization appeared to improve class enumeration accuracy relative to all other specifications. Overall, results demonstrate the importance of accurately modeling count indicators in mixture analysis, as misspecification and categorizing data can distort study outcomes.     

Bystander Response to Sexual Assault Disclosures in the U.S. Military: Encouraging Survivors to use Formal Resources

Experiencing military sexual assault (MSA) results in serious mental health consequences. Sexual assault survivors often disclose to informal sources of support, and how these individuals respond can have a significant effect on survivors’ wellbeing. Bystander intervention is one mechanism through which institutions, such as the U.S. Military, aim to teach informal support providers to respond positively and effectively to sexual assaults. One bystander response that survivors may find helpful is the discussion of formal resources (e.g., counseling options, reporting options). The current study examined factors associated with U.S. Service members’ intentions to encourage sexual assault survivors to report and seek mental health counseling, including individual characteristics (rank, gender, personal experience of MSA) and perceptions of military sexual assault response efforts (exposure to sexual assault training, leader response to sexual assault, service barriers). The study also examined contextual factors (branch) and interactions between individual and contextual predictors. We analyzed survey data from 27,505 active duty Service members collected by the U.S. Department of Defense. As expected, rank, gender, experience of MSA, training exposure, leader response, and service barriers were associated with Service members’ intentions to encourage MSA survivors to report and seek‐help. Bystander responses to disclosures can have a significant effect on survivors’ response to the assault, and these findings can help in identifying why bystanders may or may not encourage the use of formal resources after receiving a sexual assault disclosure.     

Influence of in utero exposure to maternal depression and natural disaster-related stress on infant temperament at 6 months: The children of Superstorm Sandy

This study examined the effects of in utero exposure to maternal depression and Superstorm Sandy, a hurricane that hit metropolitan New York in 2012, on infant temperament at 6 months. Temperament was assessed using the Infant Behavior Questionnaire-Revised. Maternal depression was measured by the Edinburgh Postnatal Depression Scale. The main effects and the interaction of maternal depression and Sandy exposure on infant temperament were examined using a multivariable generalized linear model. Results show that prenatal maternal depression was associated with lower emotion regulation and greater distress. Stratification and interaction analyses suggested that the adverse effects of prenatal maternal depression on problematic temperament were amplified by in utero Sandy exposure. This study underscores the importance of providing prenatal screening and treatment for maternal depression during pregnancy while also identifying high-risk families who may have suffered from disaster-related traumas to provide necessary services. As the frequency of natural disasters may increase due to climate change, it is important to understand the consequences of in utero stress on child development and to formulate plans for early identification.     

Machine learning in suicide science: Applications and ethics

For decades, our ability to predict suicide has remained at near‐chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an introduction to machine learning and its potential application to open questions in suicide research. Although only a few studies have implemented machine learning for suicide prediction, results to date indicate considerable improvement in accuracy and positive predictive value. Potential barriers to algorithm integration into clinical practice are discussed, as well as attendant ethical issues. Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored.