Prognostic models can support prevention of mental health complications after mild traumatic brain injury (mTBI). The present study aimed to identify risk factors and develop prognostic model(s) for mental health complications following mTBI. This secondary analysis of data from a randomized controlled trial included 513 adults presenting to emergency departments/urgent care centers. Candidate predictors were demographic, injury-related and health history information collected during medical chart review and eligibility screening, and scores on questionnaires completed at 2 weeks postinjury. The primary outcome was presence/absence of new or worsened major depressive disorder, anxiety disorders, and post-traumatic stress disorder (PTSD), determined with a structured psychodiagnostic interview (Mini International Neuropsychiatric Interview) at 3 and 6 months after mTBI. Logistic regression assessed the prognostic value of 22 pre-, peri-, and early postinjury factors. Least absolute shrinkage and selection operator (LASSO) was used to select predictors in prognostic model development. Younger age, identifying as a person of color, prior mTBI(s), maladaptive illness perceptions, and greater PTSD, and depression and anxiety symptom severity measured at 2 weeks postinjury were significant predictors of new/worsened mental health complications 3-6 months following mTBI. A comprehensive model (with 9 LASSO-selected predictors) showed strong discriminability for predicting mental health complications (optimism-corrected area under the receiver operating characteristic curve [AUC] = 0.80), outperforming a basic model that included only variables commonly collected as part of usual clinical care (optimism-corrected AUC = 0.71). Certain pre-injury and demographic characteristics are associated with increased risk of mental health complications after mTBI. Assessing for early postinjury illness beliefs and psychological symptoms can further improve prognostic accuracy.

Binding content together in memory (i.e., associative memory) is often impaired by negative emotion, and adults exposed to childhood adversity tend to show heightened emotional reactivity that may influence memory for emotional content. We tested whether childhood adversity augments the impairing effect of emotion on associative memory. In an online study, young adult participants ( N = 700) self-reported exposure to childhood adversity. Participants were then presented with images stratified by emotion (negative, neutral) that were paired with an image of a benign object. Twenty-four hours later, participants’ associative memory for image pairs was tested. Although childhood adversity was prevalent and negatively associated with psychological well-being, it was not associated with poorer associative memory regardless of stimuli valence ( b = −0.01, p = .175). Findings suggest that childhood adversity is not always related to associative memory despite theories positing that poor associative memory may drive mental-health concerns associated with childhood adversity.

Autistic traits are associated with differential processing of emotional and social cues. By contrast little is known about the relationship of autistic traits to socio-emotional memory, though research suggests an integral relationship between episodic memory processes and psychosocial well-being. Using an experimental paradigm, we tested if autistic traits moderate the effects of negative emotion and social cues on episodic memory (i.e. memory for past events). Young adults (N = 706) with varied levels of self-reported autistic traits (24% in clinical range) encoded images stratified by emotion (negative, neutral) and social cues (social, non-social) alongside a neutral object. After 24 h, item memory for images and associative memory for objects was tested. For item memory, after controlling for anxiety, a small effect emerged whereby a memory-enhancing effect of social cues was reduced as autistic traits increased. For associative memory, memory for pairings between neutral, but not negative, images reduced as autistic traits increased. Results suggest autistic traits are associated with reduced ability to bind neutral items together in memory, potentially impeding nuanced appraisals of past experience. This bias toward more negative, less nuanced memories of past experience may represent a cognitive vulnerability to social and mental health challenges commonly associated with autistic traits and a potential intervention target.

Background Inflammatory bowel disease (IBD) is a chronic autoimmune disease often diagnosed during adolescence. IBD negatively impacts all aspects of health-related quality of life, resulting in physical, emotional, social, school, and work functioning challenges. Adolescents have identified the need for peer support in managing their disease and promoting positive health outcomes. However, studies have yet to explore the peer support needs of this population. The study aimed to capture whether a peer mentoring program (iPeer2Peer©) was successful at improving adolescent disease self-management. The study also aimed to understand the lived experiences of adolescents participating in iPeer2Peer©. Methods Adolescents with IBD were recruited from three tertiary hospitals in Canada. The study adopted a waitlist pilot randomized control trial (RCT) with participants randomly allocated to the intervention (iPeer2Peer© program) or control group. Participants completed questionnaire measures at baseline and post-program examining self-reported self-management, self-efficacy, emotional distress, social support, and health-related quality of life. A subset of intervention participants were randomly invited post-program to participate in a semi-structured interview to examine mentee program experiences Results The quantitative analyses showed no significant differences between the intervention and control group. Three themes were identified in the qualitative analysis: 1) forming a connection over shared experiences and beyond, 2) improving mentee program experience, and 3) program flexibility. Conclusion Despite the lack of significant quantitative outcomes, qualitative data suggests peer support for adolescents with IBD adds value to IBD care by developing a sense of belonging with peers who share lived experiences. This study demonstrates the complexity of mentee psychosocial needs and challenges in measuring outcomes in peer support research. Clinical implications and future research opportunities are discussed.

This study examines the emotional consequences of spending choices in everyday life across a diverse multinational sample. Based on a dataset of 200 participants across 7 countries who received $10,000 USD, we analyzed how happy they felt from different types of purchases made with that money. Participants derived high levels of happiness from some types of purchases that have been examined in past research (e.g., buying experiences), but also from other purchases (e.g., education) that have not been the focus of previous work. We found some evidence that the emotional benefits of spending choices varied depending on whether participants lived in higher vs. lower-income countries; specifically, we found differences in the benefits of spending on gifts, housing, debt, and time-saving services. Around the world, people who spent money in ways that made them happy experienced greater improvements in overall subjective well-being 3 and 6 months later.

The COVID-19 pandemic left many people grieving multiple deaths and at risk for developing symptoms of complicated grief (CG). The present study is a prospective examination of the role of neuroticism and social support in the development of CG symptoms. Findings from cross-classified multilevel models pointed to neuroticism as a risk factor for subsequent CG symptoms. Social support had a stress-buffering effect, emerging as a protective factor following the loss of a first degree relative. More recent loss and younger age of the deceased were both independently associated with heightened CG symptoms. Results from the present study provide insight into heterogeneity in CG symptom development at the between-person level, and variability in CG symptoms within individuals in response to different deaths. Findings could therefore aid in the identification of those at risk for the development of CG symptoms.

Background and aims:This study characterized chasing behaviour as the time to return to an online gambling website after a losing or a winning visit. Methods:We analyzed a naturalistic dataset from an eCasino (PlayNow.com, the provincial platform for British Columbia, Canada), comprising 1,909,681 sessions from 15,544 individuals. Analyses distinguished sessions on slot machines, blackjack, roulette, video poker, probability games, or mixed-category sessions. Results:Overall, gamblers on most games returned more slowly as a function of the prior loss, and more quickly as a function of the prior win. Loss chasing intensities in blackjack, probability, video poker, and mixed sessions did not differ significantly from slot machines, but roulette was associated with shorter intervals to return (b = -0.13, p < 0.001). Similarly, win chasing did not vary across slot machines, blackjack, probability games, and video poker, but roulette (b = -0.08, p < 0.001) and mixed (b = -0.02, p = 0.009) sessions were associated with shorter intervals. Discussion and conclusions:The average behavioural patterns provide limited evidence for loss chasing but clearly indicate win chasing. Although slot machines are commonly considered a high-risk product, roulette in our analyses was associated with the greatest chasing intensities.

Chasing refers to the escalation of betting behaviour. It is conventionally seen when losing but can also be seen after wins. Diagnostic and screening items for gambling problems describe chasing as returning 'another day' to gamble. However, gamblers may also chase within sessions, and this is particularly relevant in online gambling. This study focused on two expressions of within-session chasing: (1) increasing the bet amount, or (2) a reduced probability of quitting the session, as a function of prior losses or wins. These expressions were examined across five online gambling products: slot machines, probability games, blackjack, video poker, and roulette. Our results showed that gamblers bet more and played longer sessions after immediate losses, but they bet less and played shorter sessions when losing cumulatively. The reversed pattern in the cumulative model may be due to financial constraints. For wins, gamblers bet more after both immediate and cumulative wins, but they also played shorter sessions. Chasing patterns were qualitatively similar by game type-with limited evidence for our hypothesis that chasing would be greatest for slot machines as an established high-risk category. Overall, chasing is multi-faceted, varying across the behavioural expressions, by the immediate or cumulative timeframe of prior outcomes, and by game type.

Continued gambling despite negative consequences, commonly known as ‘chasing’, is a defining feature of disordered gambling. Yet chasing is also a complex and multi-faceted behavioural phenotype; for example, gamblers may chase winning outcomes as well as losses. This study characterized between-session chasing behavior in a large naturalistic dataset of online gambling data, comprising 1,909,681 eCasino sessions played by 15,544 individuals on PlayNow.com, the provincial online gambling platform in British Columbia, Canada. Analyses distinguished sessions on slot machines (as the reference category), blackjack, roulette, video poker, probability games, or mixed sessions. Overall, gamblers returned more slowly after losing sessions, and more quickly after winning sessions, across most product categories. For every standard deviation increase in the prior session net loss, slot machine gamblers took 8.59% longer to return to the website (b = 0.08, p < .001). For every standard deviation increase in the prior session net win, slot machine gamblers returned 6.68% faster (b = -0.07, p < .001). Loss chasing intensities in blackjack, probability, video poker, and mixed sessions did not differ significantly from slot machine sessions, but roulette was associated with a shorter interval to return (b = -0.13, p < .001). Similarly, win chasing intensities across blackjack, probability games, and video poker did not differ significantly from slot machine sessions, but roulette (b = -0.08, p < .001) and mixed (b = -0.02, p = 0.009) sessions were associated with shorter intervals to return. Average behavioural patterns provide limited evidence for loss chasing in the interval between sessions, but gamblers return faster after larger wins. Although slot machines are commonly considered as high-risk gambling products, in our analyses online roulette was associated with the greatest chasing intensities.

Applications of multilevel models (MLMs) with three or more levels have increased alongside expanding software capability and dataset availability. Though researchers often express interest in R-squared measures as effect sizes for MLMs, R-squareds previously proposed for MLMs with three or more levels cover a limited subset of choices for how to quantify explained variance in these models. Additionally, analytic relationships between total and level-specific versions of MLM R-squared measures have not been clarified, despite such relationships becoming increasingly important to understand when there are more levels. Furthermore, the impact of predictor centering strategy on R-squared computation and interpretation has not been explicated for MLMs with any number of levels. To fill these gaps, we extend the Rights and Sterba two-level MLM R-squared framework to three or more levels, providing a general set of measures that includes preexisting three-level measures as special cases and yields additional results not obtainable from existing measures. We mathematically and pedagogically relate total and level-specific R-squareds, and show how all total and level-specific R-squared measures in our framework can be computed under any centering strategy. Finally, we provide and empirically demonstrate software (available in the r2mlm R package) to compute measures and graphically depict results.

For multilevel models (MLMs) with fixed slopes, it has been widely recognized that a level-1 variable can have distinct between-cluster and within-cluster fixed effects, and that failing to disaggregate these effects yields a conflated, uninterpretable fixed effect. For MLMs with random slopes, however, we clarify that two different types of slope conflation can occur: that of the fixed component (termed fixed conflation) and that of the random component (termed random conflation). The latter is rarely recognized and not well understood. Here we explain that a model commonly used to disaggregate the fixed component-the contextual effect model with random slopes-troublingly still yields a conflated random component. Negative consequences of such random conflation have not been demonstrated. Here we show that they include erroneous interpretation and inferences about the substantively important extent of between-cluster differences in slopes, including either underestimating or overestimating such slope heterogeneity. Furthermore, we show that this random conflation can yield inappropriate standard errors for fixed effects. To aid researchers in practice, we delineate which types of random slope specifications yield an unconflated random component. We demonstrate the advantages of these unconflated models in terms of estimating and testing random slope variance (i.e., improved power, Type I error, and bias) and in terms of standard error estimation for fixed effects (i.e., more accurate standard errors), and make recommendations for which specifications to use for particular research purposes.

Methodologists have often acknowledged that, in multilevel contexts, level-1 variables may have distinct within-cluster and between-cluster effects. However, a prevailing notion in the literature is that separately estimating these effects is primarily important when there is specific interest in doing so. Consequently, in practice, researchers uninterested in disaggregating these effects (or unaware of their difference) routinely fit models that conflate them. Furthermore, even researchers who properly disaggregate the fixed components in a model (avoid fixed conflation) may still inadvertently and unknowingly conflate the random effects (fail to avoid random conflation). The purpose of this article is to elucidate an unappreciated consequence of such fixed or random conflation, namely, that it can cause systematic distortion in all variance components, yielding uninterpretable variances that adversely affect the entire model. In this article, I provide novel mathematical derivations, simulations, and pedagogical illustrations of such variance distortion, showing how it leads to several aberrant consequences: (1) error variances at level-1 and level-2 can systematically increase (in the population) with the addition of predictors; (2) there can be a large apparent degree of between-cluster random-effect variability in cases in which there is actually no between-cluster outcome variability; (3) R-squared measures of explained variance can be severely biased, uninterpretable, and well below the logical bound of 0; and (4) inference for all fixed components of the model-not just the conflated slopes themselves-can be compromised. I conclude with recommendations for practice, including cautionary notes on interpreting results from prior research that had specified conflated slopes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

In psychology and other fields, data often have a cross-classified structure, whereby observations are nested within multiple types of non-hierarchical clusters (e.g., repeated measures cross-classified by persons and stimuli). This paper discusses ways that, in cross-classified multilevel models, slopes of lower-level predictors can implicitly reflect an ambiguous blend of multiple effects (for instance, a purely observation-level effect as well as a unique between-cluster effect for each type of cluster). The possibility of conflating multiple effects of lower-level predictors is well recognized for non-cross-classified multilevel models, but has not been fully discussed or clarified for cross-classified contexts. Consequently, in published cross-classified modeling applications, this possibility is almost always ignored, and researchers routinely specify models that conflate multiple effects. In this paper, we show why this common practice can be problematic, and show how to disaggregate level-specific effects in cross-classified models. We provide a novel suite of options that include fully cluster-mean-centered, partially cluster-mean-centered, and contextual effect models, each of which provides a unique interpretation of model parameters. We further clarify how to avoid both fixed and random conflation, the latter of which is widely misunderstood even in non-cross-classified models. We provide simulation results showing the possible deleterious impact of such conflation in cross-classified models, and walk through pedagogical examples to illustrate the disaggregation of level-specific effects. We conclude by considering additional model complexities that can arise with cross-classification, providing guidance for researchers in choosing among model specifications, and describing newly available software to aid researchers who wish to disaggregate effects in practice.

Multilevel regression mixtures involving both discrete latent classes and continuous random effects are an increasingly popular approach for accommodating nested data structures. However, their application has outpaced the development of effect size measures to aid model interpretation. In response, we provide a general framework of R-squared measures for multilevel regression mixtures with random effects as well as either classes only at level-1 (L1MIX), or classes only at level-2 (L2MIX), or classes at both levels (L1L2MIX). This work extends and unites a previous suite of R-squared measures for multilevel mixtures with latent classes but no random effects (Rights & Sterba, 2018) and a suite of R-squared measures for multilevel models with random effects but no latent classes (Rights & Sterba, 2019).The general framework provided here includes total and class-specific measures that each allow the researcher to distinguish among distinct sources of explained variance in the fitted model. We provide software for implementing these measures and provide two illustrative empirical examples.

Background The incidence of depression in human females rises steadily throughout adolescence, a critical period of pubertal maturation marked by increasing levels of gonadal hormones including estrogens and progesterone. These gonadal hormones play a central role in social and emotional development and may also contribute to the increased occurrence of depression in females that begins in early adolescence. In this study, we examine whether and how introducing synthetic estrogen and progestin derivatives through the use of combined hormonal contraceptives (CHC), affects adolescent females' risk for developing depression. We further assess potential links between CHC use and alterations in stress responses and social-emotional functioning. Methods Using a longitudinal cohort design, we will follow a sample of adolescent females over the span of three years. Participants will be assessed at three time points: once when they are between 13 and 15 years of age, and at approximately 18 and 36 months after their initial assessment. Each time point will consist of two online sessions during which participants will complete a clinical interview that screens for key symptoms of mental health disorders, along with a series of questionnaires assessing their level of depressive symptoms and history of contraceptive use. They will also complete a standardized social-evaluative stress test and an emotion recognition task, as well as provide saliva samples to allow for assessment of their circulating free cortisol levels. Discussion In this study we will assess the effect of CHC use during adolescence on development of Major Depressive Disorder (MDD). We will control for variables previously found to or proposed to partially account for the observed relationship between CHC use and MDD, including socioeconomic status, age of sexual debut, and CHC-related variables including age of first use, reasons for use, and its duration. In particular, we will discover whether CHC use increases depressive symptoms and/or MDD, whether elevated depressive symptoms and/or MDD predict a higher likelihood of starting CHC, or both. Furthermore, this study will allow us to clarify whether alterations in stress reactivity and social-emotional functioning serve as pathways through which CHC use may result in increased risk of depressive symptoms and/or MDD.

Multilevel models are used ubiquitously in the social and behavioral sciences and effect sizes are critical for contextualizing results. A general framework of R-squared effect size measures for multilevel models has only recently been developed. Rights and Sterba (2019) distinguished each source of explained variance for each possible kind of outcome variance. Though researchers have long desired a comprehensive and coherent approach to computing R-squared measures for multilevel models, the use of this framework has a steep learning curve. The purpose of this tutorial is to introduce and demonstrate using a new R package – r2mlm – that automates the intensive computations involved in implementing the framework and provides accompanying graphics to visualize all multilevel R-squared measures together. We use accessible illustrations with open data and code to demonstrate how to use and interpret the R package output.

Experiencing stressors related to the COVID-19 pandemic such as health-related concern, social isolation, occupational disruption, financial insecurity, and resource scarcity can adversely impact mental health; however, the extent of the impact varies greatly between individuals. In this study, we examined the role of neuroticism as an individual-level risk factor that exacerbates the association between pandemic stressors and depressive symptoms. With repeated assessments of pandemic stressors and depressive symptoms collected from 3181 participants over the course of the pandemic, we used multilevel modeling to test if neuroticism moderated the association between pandemic stressors and depressive symptoms at both between- and within-person levels. At the between-person level, we found that participants who reported more pandemic stressors on average had higher levels of depressive symptoms and that this association was stronger among those high in neuroticism. At the within-person level, reporting more pandemic stressors relative to one's average on any given occasion was also associated with heightened depressive symptoms and this effect was similarly exacerbated by neuroticism. The findings point to pandemic stressor exposure and neuroticism as risk factors for depressive symptoms and, in demonstrating their synergistic impact, may help identify individuals at greatest risk for adverse psychological responses to the COVID-19 pandemic.

Developmental researchers commonly utilize multilevel models (MLMs) to describe and predict individual differences in change over time. In such growth model applications, researchers have been widely encouraged to supplement reporting of statistical significance with measures of effect size, such as R-squareds (R2 ) that convey variance explained by terms in the model. An integrative framework for computing R-squareds in MLMs with random intercepts and/or slopes was recently introduced by Rights and Sterba and it subsumed pre-existing MLM R-squareds as special cases. However, this work focused on cross-sectional applications, and hence did not address how the computation and interpretation of MLM R-squareds are affected by modeling considerations typically arising in longitudinal settings: (a) alternative centering choices for time (e.g., centering-at-a-constant vs. person-mean-centering), (b) nonlinear effects of predictors such as time, (c) heteroscedastic level-1 errors and/or (d) autocorrelated level-1 errors. This paper addresses these gaps by extending the Rights and Sterba R-squared framework to longitudinal contexts. We: (a) provide a full framework of total and level-specific R-squared measures for MLMs that utilize any type of centering, and contrast these with Rights and Sterba's measures assuming cluster-mean-centering, (b) explain and derive which measures are applicable for MLMs with nonlinear terms, and extend the R-squared computation to accommodate (c) heteroscedastic and/or (d) autocorrelated errors. Additionally, we show how to use differences in R-squared (ΔR2 ) measures between growth models (adding, for instance, time-varying covariates as level-1 predictors or time-invariant covariates as level-2 predictors) to obtain effects sizes for individual terms. We provide R software (r2MLMlong) and a running pedagogical example analyzing growth in adolescent self-efficacy to illustrate these methodological developments. With these developments, researchers will have greater ability to consider effect size when analyzing and predicting change using MLMs.