Weight stigmatization is related to emotional and psychological distress including low self-esteem, body image dissatisfaction, depression, and anxiety; all linked with suboptimal breastfeeding outcomes. This qualitative descriptive study explored postpartum individuals’ recalled experiences of weight stigma during interactions with perinatal healthcare professionals and its perceived influence on their breastfeeding experiences. Semi-structured phone interviews were conducted with (n= 18) participants. Three themes emerged: (1) “Size Doesn’t Matter: They Looked Beyond the Scale,” (2) “My Self-Confidence and Desire to Breastfeed is More Important than Weight,” and (3) “I Was on My Own”— Limited Social Support not Weight Stigma Influenced Breastfeeding.
The purpose of this study was to examine the collective effect of a symptom cluster (depression, anxiety, fatigue, and impaired sleep quality) at baseline on the quality of life (QOL) of patients with type 2 diabetes (T2DM) over time. Methods This was a secondary data analysis of 302 patients with T2DM who presented with both hypertension and hyperlipidemia. All of the participants were enrolled in a randomized controlled intervention study testing strategies to improve medication adherence. The psychological symptoms and QOL were assessed at baseline, 6 months, and 12 months. Cluster analysis was used to identify subgroups of patients based on the severity of symptoms at baseline. Results Hierarchical cluster analysis identified 4 patient subgroups: all low severity, mild, moderate, and all high severity. There were significant differences in patients' QOL overall among the 4 subgroups. Compared with the all-low-severity subgroup, subgroups with higher severity of the 4 symptoms had poorer QOL across all 3 time points. QOL was most impacted by trait anxiety across the 3 time points.
This paper explores relationships amongst cross-lagged models allowing trajectories to be freely estimated, some accounting for time-varying differences amongst individuals (Autoregressive Latent Trajectory (ALT), General Cross-lagged Model (GCLM), and Latent Growth Curve Model with Structured Residuals and Unspecified Growth Trajectory (LGCM-SR-UGT)) and some not (Cross-lagged Panel Model (CLPM), Random Intercept Cross-lagged Panel Model (RI-CLPM), and Mean Stationary GCLM). An applied example using LSAY data demonstrates these models. Simulations examine (1) fit indices assessing "good" fit and Bayes Factor for model selection; (2) consequences of ignoring variability in trajectories on cross-lagged estimates. Findings were (1) RMSEA discerned "good" fit and Bayes Factor tended to select models closely related to true model over less related models; (2) various patterns of bias in path estimates and standard errors are found, in particular, causal dominance in conjunction with time-variant between-person variance and covariance were notably influential on bias in path estimates.
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