Background Penalised regression methods are a useful atheoretical approach for both developing predictive models and selecting key indicators within an often substantially larger pool of available indicators. In comparison to traditional methods, penalised regression models improve prediction in new data by shrinking the size of coefficients and retaining those with coefficients greater than zero. However, the performance and selection of indicators depends on the specific algorithm implemented. The purpose of this study was to examine the predictive performance and feature (i.e., indicator) selection capability of common penalised logistic regression methods (LASSO, adaptive LASSO, and elastic-net), compared with traditional logistic regression and forward selection methods. Design Data were drawn from the Australian Temperament Project, a multigenerational longitudinal study established in 1983. The analytic sample consisted of 1,292 (707 women) participants. A total of 102 adolescent psychosocial and contextual indicators were available to predict young adult daily smoking. Findings Penalised logistic regression methods showed small improvements in predictive performance over logistic regression and forward selection. However, no single penalised logistic regression model outperformed the others. Elastic-net models selected more indicators than either LASSO or adaptive LASSO. Additionally, more regularised models included fewer indicators, yet had comparable predictive performance. Forward selection methods dismissed many indicators identified as important in the penalised logistic regression models. Conclusions Although overall predictive accuracy was only marginally better with penalised logistic regression methods, benefits were most clear in their capacity to select a manageable subset of indicators. Preference to competing penalised logistic regression methods may therefore be guided by feature selection capability, and thus interpretative considerations, rather than predictive performance alone.

Background: Penalised regression methods are a useful atheoretical approach for identifying key predictive indicators when one’s initial list of indicators is substantial, a process which may aid in informing population health surveillance. The purpose of this study was to examine the predictive performance and feature (i.e., variable) selection capability of common penalised regression methods (LASSO, adaptive LASSO, and elastic-net), compared with traditional logistic regression and forward selection methods. Design: Data were drawn from the Australian Temperament Project, a longitudinal cohort study beginning in 1983. The analytic sample consisted of 1,292 (707 women) participants. A total of 102 adolescent psychosocial and contextual indicators were available to predict young adult daily smoking. Findings: Penalised regression methods showed small improvements in predictive performance over logistic regression and forward selection. However, no single penalised regression model outperformed the others. Elastic-net models selected more indicators than either LASSO or adaptive LASSO. Additionally, more regularised models included fewer indicators, yet had comparable predictive performance. Forward selection methods dismissed many indicators identified as important in the penalised regression models. Conclusions: Although overall predictive accuracy was only marginally better with penalised regression method, benefits were most clear in their capacity to select a manageable subset of indicators. Preference to competing penalised regression methods may therefore be guided by feature selection capability, and thus interpretative considerations, rather than predictive performance alone.

Parenting interventions offer an evidence-based method for the prevention and early intervention of child mental health problems, but to-date their population-level effectiveness has been limited by poor reach and engagement, particularly for fathers, working mothers, and disadvantaged families. Tailoring intervention content to parents’ context offers the potential to enhance parent engagement and learning by increasing relevance of content to parents’ daily experiences. However, this approach requires a detailed understanding of the common parenting situations and issues that parents face day-to-day, which is currently lacking. We sought to identify the most common parenting situations discussed by parents on parenting-specific forums of the free online discussion forum, Reddit. We aimed to understand perspectives from both mothers and fathers, and thus retrieved publicly available data from r/Daddit and r/Mommit. We used latent Dirichlet allocation to identify the 10 most common topics discussed in the Reddit posts, and completed a manual text analysis to summarize the parenting situations (defined as involving a parent and their child aged 0–18 years, and describing a potential/actual issue). We retrieved 340 (r/Daddit) and 578 (r/Mommit) original posts. A model with 31 latent Dirichlet allocation topics was best fitting, and 24 topics included posts that met our inclusion criteria for manual review. We identified 45 unique but broadly defined parenting situations. The majority of parenting situations were focused on basic childcare situations relating to eating, sleeping, routines, sickness, and toilet training; or related to how to respond to child negative emotions or difficult behavior. Most situations were discussed in relation to infant or toddler aged children, and there was high consistency in the themes raised in r/Daddit and r/Mommit. Our results offer potential to tailor parenting interventions in a meaningful way, creating opportunities to develop content and resources that are directly relevant to parents’ lived experiences.

Purpose Maternal psychological distress and mother-infant bonding problems each predict poorer offspring outcomes. They are also related to each other, yet the extensive literature reporting their association has not been meta-analysed. Methods We searched MEDLINE, PsycINFO, CINAHL, Embase, ProQuest DTG, and OATD for English-language peer-reviewed and grey literature reporting an association between mother-infant bonding, and multiple indicators of maternal psychological distress. Results We included 133 studies representing 118 samples; 99 samples (110,968 mothers) were eligible for meta-analysis. Results showed concurrent associations across a range of timepoints during the first year postpartum, between bonding problems and depression (r = .27 [95% CI 0.20, 0.35] to r = .47 [95% CI 0.41, 0.53]), anxiety (r = .27 [95% CI 0.24, 0.31] to r = .39 [95% CI 0.15, 0.59]), and stress (r = .46 [95% CI 0.40, 0.52]). Associations between antenatal distress and subsequent postpartum bonding problems were mostly weaker and with wider confidence intervals: depression (r = .20 [95% CI 0.14, 0.50] to r = .25 [95% CI 0.64, 0.85]), anxiety (r = .16 [95% CI 0.10, 0.22]), and stress (r = .15 [95% CI − 0.67, 0.80]). Pre-conception depression and anxiety were associated with postpartum bonding problems (r = − 0.17 [95% CI − 0.22, − 0.11]). Conclusion Maternal psychological distress is associated with postpartum mother-infant bonding problems. Co-occurrence of psychological distress and bonding problems is common, but should not be assumed. There may be benefit in augmenting existing perinatal screening programs with well-validated mother-infant bonding measures.

Background: Young adults regularly using cannabis represent a uniquely vulnerable yet heterogeneous cohort. Few studies have examined user profiles using cannabis use motives and expectations. The association between user profiles and psychosocial functioning among only regular users remains unexplored. This exploration is important to improve public education efforts and design tailor treatment approaches.Methods: Regular cannabis users (at least weekly; n = 329) completed an online survey via Amazon Mechanical Turk. The survey measured levels of cannabis use, other substance use, motives and expectations of cannabis use, symptoms of psychosis, depression, anxiety and stress, and reckless behavior such as getting high before work or driving under the influence of cannabis. Latent class analysis was performed using motives and expectations to identify data driven patterns of regular cannabis use. Classes were then used to investigate mental health and behavioral correlates of differences in motives and expectations.Results: A 2-class solution provided the best fit to the data; Class 1: Low Motives and Expectancies (n = 158) characterized by lower endorsement across all motivation and expectation variables, and Class 2: High Motives and Expectancies (n = 171) characterized by endorsing multiple motivations, and higher positive and negative expectations of cannabis use. Classes differed in a range of cannabis use variables; e.g., greater proportion of peer use in Class 2. The High Motives and Expectancies users reported higher symptoms of psychosis (positive and negative symptoms), depression, anxiety, and stress. A higher proportion met the criteria for a cannabis use disorder compared with Low Motives and Expectancies users. High Motives and Expectancies users reported higher mean problems with nicotine dependence and illicit drug use other than cannabis and were more likely to get high before work and drive under the influence of cannabis.Conclusions: There is heterogeneity among young regular cannabis users in their motivations and expectancies of use and associated psychosocial functioning. Understanding motives and expectancies can help segregate which users are at higher risk of worse functioning. These findings are timely when designing targeted assessment and treatment strategies, particularly as cannabis is further decriminalized and accessibility increases.