YOUTH AND COMMUNITY BASED PARTICIPATORY RESEARCH: SUGGESTIONS FOR FUTURE DIRECTIONS

Community Based Participatory Research (CBPR) is becoming popular in the field of public health. While many studies focus on including adults in the CBPR process, far fewer have utilized the input and expertise of children and adolescents. Successful CBPR projects are important to the field of public health due to their ability to open the doorway to the health needs and experiences of youth. This review paper first provides relevant background information regarding the health of youth living in the United States, the importance of adopting a life course approach to public health interventions, and the field of Community Based Participatory Research. It then explains the review methodology utilized and presents the results of the literature review. Specific attention is paid to summarizing CBPR work conducted with youth and the key issues related to such work. This review concludes by providing suggestions for future successful applications of CBPR with youth populations, and discusses the benefits and challenges of conducting this type of research. Results from this review contribute to a better understanding of the importance of utilizing ideas and insights of young people in order to create programming and policy changes that effectively address their needs

Contraceptive and family planning experiences, priorities, and preferences of women with serious mental illness

Over three million women of reproductive age residing in the United States are living with a serious mental illness (SMI). Compared to the general population, women with SMI have lower rates of contraceptive use, receive suboptimal reproductive care, and are at increased risk for unintended pregnancy. Few studies examine the factors associated with contraceptive nonuse/misuse and unintended pregnancy among this population or what women with SMI identify as important contraceptive and family planning counseling topics. A systematic literature review was conducted in Study 1 to understand the current state of research regarding the contraceptive experiences, knowledge, and attitudes of women with SMI. Research quality, outcomes assessed, and findings varied, but many studies confirmed that women with SMI have disproportionately poorer contraceptive experiences. In Study 2, qualitative interviews were facilitated with women with SMI to understand factors influencing their contraceptive use/nonuse. Findings revealed that pregnancy intentions did not always align with contraceptive behaviors. Contributing factors to contraceptive nonuse/misuse included the influence of SMI symptoms, knowledge of and attitudes toward contraception, reproductive coercion, and sexual violence. Study 3 examined women’s reproductive counseling preferences and priorities, which included consideration of SMI symptoms when making contraceptive decisions, SMI medication contraindications and teratogenicity, SMI symptom changes during pregnancy/post-partum, and provider sensitivity. Additionally, these studies uncovered a more robust understanding of the differential reproductive experiences of women with bipolar, major depressive, and schizophrenia/schizoaffective disorders. Primary differences included variations in SMI symptoms and their influence on contraceptive use, with bipolar mania contributing to nonuse and fear of exacerbations in depressive symptoms impacting contraceptive method choice. Further, a higher proportion of women with schizophrenia/schizoaffective disorder experienced reproductive coercion, increasing their risk for unintended pregnancy. This dissertation provides important information that can enhance the way reproductive services are delivered to women with SMI. All women deserve the opportunity to express their sexuality and realize their family planning goals, however they may be defined. To promote the reproductive health and rights of this population, it is of significant public health importance to ensure that women with SMI can safely and effectively prevent or experience pregnancy and achieve optimal fertility-related outcomes

Matrix-free hydrodynamic study on the size distribution and conformation of three technical lignins from wood and non-wood

Molecular weight (MW) and related conformational data of three commercially available technical lignins (Alcell L, kraft L, and soda L) have been studied by means of analytical ultracentrifugation, taking advantage of some recent developments in both sedimentation velocity and sedimentation equilibrium determinations. The lignins were dissolved in dimethyl sulphoxide (with ca. 90% solubility), and solutions were studied with regards to their oligomeric state, heterogeneity profiles (distribution of sedimentation coefficients), and molecular weight distributions (MWD). Alcell L and soda L have similar properties showing one major low MW component and two minor high MW components, whereas kraft L appears to be larger and more uniform, i.e., it shows a more monodisperse MWD. Weight average molecular weight (Mw) data from sedimentation equilibrium obtained by the new SEDFIT-MSTAR procedure in conjunction with MULTISIG analysis were found to be ~18 kDa (Alcell L), 25 kDa (kraft L), and 15 kDa (soda L). Further analysis of the data by means of the routines MULTISIG and M_INVEQ confirmed the presence of additional components in Alcell L and soda L, and the larger size and high degree of monodispersity of kraft L. The intrinsic viscosity data of the three lignins were found to be very similar in the range of 22–24 ml g-1, and all data were consistent with an elongated plate shape molecular structure with an equivalent discoid aspect ratio ~30

MultiSig: a new high-precision approach to the analysis of complex biomolecular systems

MultiSig is a newly developed mode of analysis of sedimentation equilibrium (SE) experiments in the analytical ultracentrifuge, having the capability of taking advantage of the remarkable precision (~0.1 % of signal) of the principal optical (fringe) system employed, thus supplanting existing methods of analysis through reducing the ‘noise’ level of certain important parameter estimates by up to orders of magnitude. Long-known limitations of the SE method, arising from lack of knowledge of the true fringe number in fringe optics and from the use of unstable numerical algorithms such as numerical differentiation, have been transcended. An approach to data analysis, akin to ‘spatial filtering’, has been developed, and shown by both simulation and practical application to be a powerful aid to the precision with which near-monodisperse systems can be analysed, potentially yielding information on protein-solvent interaction. For oligo- and poly-disperse systems the information returned includes precise average mass distributions over both cell radial and concentration ranges and mass-frequency histograms at fixed radial positions. The application of MultiSig analysis to various complex heterogenous systems and potentially multiply-interacting carbohydrate oligomers is described

Aspects of the analytical ultracentrifuge determination of the molar mass distribution of polysaccharides

Molar mass or ‘molecular weight’ is one of the most fundamental parameters describing a macromolecule. Because of their polydisperse nature, polysaccharides are usually described by distributions of molar mass. SEC-MALS (size exclusion chromatography coupled to multi-angle light scattering) is often a convenient method of choice, but there are many instances where it is unsuitable. Modern AUC (analytical ultracentrifuge) methods provide a valuable alternative – now easier to use than before – and, after briefly reviewing some older procedures, we highlight two recently published and complementary methods, namely, the ‘Extended Fujita’ approach for the analysis of sedimentation velocity data and SEDFIT-MSTAR for the analysis of sedimentation equilibrium data. Nonideality needs to be considered and can be dealt with in a standard way. These methods can also indicate if associative phenomena are present, which can then be quantified using more complex AUC algorithms

Ultracentrifuge methods for the analysis of polysaccharides, glycoconjugates, and lignins

Although like proteins, polysaccharides are synthesized by enzymes, unlike proteins there is no template. This means that they are polydisperse, do not generally have compact folded structures, and are often very large with greater nonideality behavior in solution. This chapter considers the relevant analytical ultracentrifuge methodology available for characterizing these and related carbohydrate-based systems and information this methodology supplies, in terms of sizes, shapes, and interactions using a comprehensive range of examples, including glycoconjugates and lignins. The relevance and potential of recent software developments such as SEDFIT-MSTAR, the Extended Fujita algorithm, and HYDFIT are considered

Bias-corrected and doubly robust inference for the three-level longitudinal cluster-randomized trials with missing continuous outcomes and small number of clusters: Simulation study and application to a study for adults with serious mental illnesses

Longitudinal cluster-randomized designs have been popular tools for comparative effective research in clinical trials. The methodologies for the three-level hierarchical design with longitudinal outcomes need to be better understood under more pragmatic settings; that is, with a small number of clusters, heterogeneous cluster sizes, and missing outcomes. Generalized estimating equations (GEEs) have been frequently used when the distribution of data and the correlation model are unknown. Standard GEEs lead to bias and an inflated type I error rate due to the small number of available clinics and non-completely random missing data in longitudinal outcomes. We evaluate the performance of inverse probability weighted (IPW) estimating equations, with and without augmentation, for two types of missing data in continuous outcomes and individual-level treatment allocation mechanisms combined with two bias-corrected variance estimators. Our intensive simulation results suggest that the proposed augmented IPW method with bias-corrected variance estimation successfully prevents the inflation of false positive findings and improves efficiency when the number of clinics is small, with moderate to severe missing outcomes. Our findings are expected to aid researchers in choosing appropriate analysis methods for three-level longitudinal cluster-randomized designs. The proposed approaches were applied to analyze data from a longitudinal cluster-randomized clinical trial involving adults with serious mental illnesses