Associations between maternal psychological distress and mother-infant bonding: a systematic review and meta-analysis.

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.

Examining Analytic Practices in Latent Dirichlet Allocation Within Psychological Science: Scoping Review.

BACKGROUND: Topic modeling approaches allow researchers to analyze and represent written texts. One of the commonly used approaches in psychology is latent Dirichlet allocation (LDA), which is used for rapidly synthesizing patterns of text within "big data," but outputs can be sensitive to decisions made during the analytic pipeline and may not be suitable for certain scenarios such as short texts, and we highlight resources for alternative approaches. This review focuses on the complex analytical practices specific to LDA, which existing practical guides for training LDA models have not addressed. OBJECTIVE: This scoping review used key analytical steps (data selection, data preprocessing, and data analysis) as a framework to understand the methodological approaches being used in psychology research using LDA. METHODS: A total of 4 psychology and health databases were searched. Studies were included if they used LDA to analyze written words and focused on a psychological construct or issue. The data charting processes were constructed and employed based on common data selection, preprocessing, and data analysis steps. RESULTS: A total of 68 studies were included. These studies explored a range of research areas and mostly sourced their data from social media platforms. Although some studies reported on preprocessing and data analysis steps taken, most studies did not provide sufficient detail for reproducibility. Furthermore, the debate surrounding the necessity of certain preprocessing and data analysis steps is revealed. CONCLUSIONS: Our findings highlight the growing use of LDA in psychological science. However, there is a need to improve analytical reporting standards and identify comprehensive and evidence-based best practice recommendations. To work toward this, we developed an LDA Preferred Reporting Checklist that will allow for consistent documentation of LDA analytic decisions and reproducible research outcomes.