The interpersonal and mental health implications of the coherence of coming out narratives for a sample of gay men in committed romantic relationships

The coming out process has been conceptualized as a developmental imperative for those who will eventually accept their same-sex attractions. It is widely accepted that homophobia, heterosexism, and homonegativity are cultural realities that may complicate this developmental process for gay men. The current study views coming out as an extra-developmental life task that is at best a stressful event, and at worst traumatic when coming out results in the rupture of salient relationships with parents, siblings, and/or close friends. To date, the minority stress model (Meyer, 1995; 2003) has been utilized as an organizing framework for how to empirically examine external stressors and mental health disparities for lesbians, gay men, and bisexual individuals in the United States. The current study builds on this literature by focusing on the influence of how gay men make sense of and represent the coming out process in a semi-structured interview, more specifically, by examining the legacy of the coming out process on indicators of wellness. In a two-part process, this study first employs the framework well articulated in the adult attachment literature of coherence of narratives to explore both variation and implications of the coming out experience for a sample of gay men (n = 60) in romantic relationships (n = 30). In particular, this study employed constructs identified in the adult attachment literature, namely Preoccupied and Dismissing current state of mind, to code a Coming Out Interview (COI). In the present study current state of mind refers to the degree of coherent discourse produced about coming out experiences as relayed during the COI. Multilevel analyses tested the extent to which these COI dimensions, as revealed through an analysis of coming out narratives in the COI, were associated with relationship quality, including self-reported satisfaction and observed emotional tone in a standard laboratory interaction task and self-reported symptoms of psychopathology. In addition, multilevel analyses also assessed the Acceptance by primary relationship figures at the time of disclosure, as well as the degree of Outness at the time of the study. Results revealed that participant’s narratives on the COI varied with regard to Preoccupied and Dismissing current state of mind, suggesting that the AAI coding system provides a viable organizing framework for extracting meaning from coming out narratives as related to attachment relevant constructs. Multilevel modeling revealed construct validity of the attachment dimensions assessed via the COI; attachment (i.e., Preoccupied and Dismissing current state of mind) as assessed via the Adult Attachment Interview (AAI) was significantly correlated with the corresponding COI variables. These finding suggest both methodological and conceptual convergence between these two measures. However, with one exception, COI Preoccupied and Dismissing current state of mind did not predict relationship outcomes or self-reported internalizing and externalizing symptoms. However, further analyses revealed that the degree to which one is out to others moderated the relationship between COI Preoccupied and internalizing. Specifically, for those who were less out to others, there was a significant and positive relationship between Preoccupied current state of mind towards coming out and internalizing symptoms. In addition, the degree of perceived acceptance of sexual orientation by salient relationship figures at the time of disclosure emerged as a predictor of mental health. In particular, Acceptance was significantly negatively related to internalizing symptoms. Overall, the results offer preliminary support that gay men’s narratives do reflect variation as assessed by attachment dimensions and highlights the role of Acceptance by salient relationship figures at the time of disclosure. Still, for the most part, current state of mind towards coming out in this study was not associated with relationship quality and self-reported indicators of mental health. This finding may be a function of low statistical power given the modest sample size. However, the relationship between Preoccupied current state of mind and mental health (i.e., internalizing) appears to depend on degree of Outness. In addition, the response of primary relationships figures to coming out may be a relevant factor in shaping mental health outcomes for gay men. Limitations and suggestions for future research and clinical intervention are offered.

INVESTIGATING THE USE OF MAZE-CBM FOR HIGH SCHOOL STUDENTS

Recent legislation and initiatives set forth high academic expectations for all high school graduates in the area of reading (National Governors Association Center for Best Practices, 2010; Every Student Succeeds Act, 2015). To determine which students need additional support to meet these reading standards, teachers can conduct universal screening using formative assessments. Maze Curriculum-Based Measurement (Maze-CBM) is a commonly used screening and progress monitoring assessment that the National Center on Intensive Intervention (2013) and the Center on Instruction (Torgesen & Miller, 2009) recommend. Despite the recommendation to use Maze-CBM, little research has been conducted on the reliability and validity of Maze-CBM for measuring reading ability for students at the secondary level (Mitchell & Wexler, 2016). In the papers included in this dissertation, I present an initial investigation into the use of Maze-CBM for secondary students. In the first paper, I investigated prior studies of Maze-CBM for students in Grades 6 through 12. Next, in the second paper, I investigated the alternate-form reliability and validity for screening students in Grades 9 and 10 using signal detection theory methods. In the third paper, I examined the effect of genre on Maze-CBM scores with a sample of students in Grades 9 and 10 using multilevel modeling. When writing these three papers, I discovered several important findings related to Maze-CBM. First, there are few studies that have investigated the technical adequacy of Maze-CBM for screening and progress monitoring students in Grades 6 through 12. Additionally, only two studies (McMaster, Wayman, & Cao, 2006; Pierce, McMaster, & Deno, 2010) examined the technical adequacy of Maze-CBM for high school students. A second finding is that the reliability of Maze-CBM is often below acceptable levels for making screening decisions or progress monitoring decisions (.80 and above and .90 and above, respectively; Salvia, Ysseldyke, & Bolt, 2007) for secondary students. A third finding is that Maze-CBM scores show promise of being a valid screening tool for reading ability of secondary students. Finally, I found that the genre of the text used in the Maze-CBM assessment does impact scores on Maze-CBM for students in Grades 9 and 10.

NEIGHBORHOOD PERCEPTION OF SAFETY AND THE MOBILITY GAP BETWEEN MEN AND WOMEN IN OLD AGE

Thesis (Master, Community Health & Epidemiology) -- Queen's University, 2016-10-02 21:02:07.735

The quality of the physical environment and its association with activities and well-being among older people in residential care facilities

The physical environment can influence older people’s health and well-being, and is often mentioned as being an important factor for person-centred care. Due to high levels of frail health, many older people spend a majority of their time within care facilities and depend on the physical environment for support in their daily life. However, the quality of the physical environment is rarely evaluated, and knowledge is sparse in terms of how well the environment meets the needs of older people. This is partly due to the lack of valid and reliable instruments that could provide important information on environmental quality. Aim: The aim of this thesis was to study the quality of the physical environment in Swedish care facilities for older people, and how it relates to residents’ activities and well-being. Methods: The thesis comprises four papers where both qualitative and quantitative methods were used. Study I involved the translation and adaptation of the Sheffield Care Environment Assessment Matrix (SCEAM) into a Swedish version (S-SCEAM). Several methods were used including forward and backward translation, test of validity via expert consultation and reliability tests. In Study II, S-SCEAM was used to assess the quality of the environment, and descriptive data were collected from 20 purposively sampled residential care facilities (RCFs). Study III was a comparative case study conducted at two RCFs using observations, interviews and S-SCEAM to examine how the physical environment relates to older people’s activities and interactions. In study IV, multilevel modeling was used to determine the association between the quality of the physical environment and the psychological and social well-being of older people living in RCFs. The data in the thesis were analysed using qualitative content analysis, and descriptive, bivariate and multilevel statistics. Results: A specific result was the production of the Swedish version of SCEAM. The instrument contains 210 items structured into eight domains reflecting the needs of older people. When using S-SCEAM, the results showed a substantial variation in the quality of the physical environment between and within RCFs. In general, private apartments and dining areas had high quality, whereas overall building layout and outdoor areas had lower quality. Also, older people’s safety was supported in the majority of facilities, whereas cognitive support and privacy had lower quality. Further, the results showed that environmental quality in terms of cognitive support was associated with residents’ social well-being. Specific environmental features, such as building design and space size, were also noted, through observation, as influencing residents’ activities, and several barriers were found that seemed to restrict residents’ full use of the environment. Conclusions: This thesis contributes to the growing evidence-based design field. The S-SCEAM can be used in future research on the association between the environment and people’s health and well-being. The instrument could also serve as a guide in the planning and design process of new RCFs.

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed.

Modeling, control and simulation of full-power converter wind turbines equipped with permanent magnet synchronous generator

In this paper, two wind turbines equipped with a permanent magnet synchronous generator (PMSG) and respectively with a two-level or a multilevel converter are simulated in order to access the malfunction transient performance. Three different drive train mass models, respectively, one, two and three mass models, are considered in order to model the bending flexibility of the blades. Moreover, a fractional-order control strategy is studied comparatively to a classical integer-order control strategy. Computer simulations are carried out, and conclusions about the total harmonic distortion (THD) of the electric current injected into the electric grid are in favor of the fractional-order control strategy.

Power converter topologies for wind energy conversion systems: Integrated modeling, control strategy and performance simulation

This paper presents new integrated model for variable-speed wind energy conversion systems, considering a more accurate dynamic of the wind turbine, rotor, generator, power converter and filter. Pulse width modulation by space vector modulation associated with sliding mode is used for controlling the power converters. Also, power factor control is introduced at the output of the power converters. Comprehensive performance simulation studies are carried out with matrix, two-level and multilevel power converter topologies in order to adequately assert the system performance. Conclusions are duly drawn.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.

Multilevel Modelling with Spatial Effects

In multilevel modelling, interest in modeling the nested structure of hierarchical data has been accompanied by increasing attention to different forms of spatial interactions across different levels of the hierarchy. Neglecting such interactions is likely to create problems of inference, which typically assumes independence. In this paper we review approaches to multilevel modelling with spatial effects, and attempt to connect the two literatures, discussing the advantages and limitations of various approaches.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Modeling spatial distribution of cancer incidence in Cordoba, Argentina

Background: Cancer is the second leading cause of death in Argentina, and there is little knowledge about its incidence. The first study based on population-based cancer registry described spatial incidence and indicated that there existed at least county-level aggregation. The aim of the present work is to model the incidence patterns for the most incidence cancer in Córdoba Province, Argentina, using information from the Córdoba Cancer Registry by performing multilevel mixed model approach to deal with dependence and unobserved heterogeneity coming from the geo-reference cancer occurrence. Methods: Standardized incidence rates (world standard population) (SIR) by sex based on 5-year age groups were calculated for 109 districts nested on 26 counties for the most incidence cancers in Cordoba using 2004 database. A Poisson twolevel random effect model representing unobserved heterogeneity between first level-districts and second level-counties was fitted to assess the spatial distribution of the overall and site specific cancer incidence rates. Results: SIR cancer at Córdoba province shown an average of 263.53±138.34 and 200.45±98.30 for men and women, respectively. Considering the ratio site specific mean SIR to the total mean, breast cancer ratio was 0.25±0.19, prostate cancer ratio was 0.12±0.10 and lower values for lung and colon cancer for both sexes. The Poisson two-level random intercepts model fitted for SIR data distributed with overdispersion shown significant hierarchical structure for the cancer incidence distribution. Conclusions: a strong spatial-nested effect for the cancer incidence in Córdoba was observed and will help to begin the study of the factors associated with it.

A MULTILEVEL MODEL TO ADDRESS BATCH EFFECTS IN COPY NUMBER ESTIMATION USING SNP ARRAYS

Submicroscopic changes in chromosomal DNA copy number dosage are common and have been implicated in many heritable diseases and cancers. Recent high-throughput technologies have a resolution that permits the detection of segmental changes in DNA copy number that span thousands of basepairs across the genome. Genome-wide association studies (GWAS) may simultaneously screen for copy number-phenotype and SNP-phenotype associations as part of the analytic strategy. However, genome-wide array analyses are particularly susceptible to batch effects as the logistics of preparing DNA and processing thousands of arrays often involves multiple laboratories and technicians, or changes over calendar time to the reagents and laboratory equipment. Failure to adjust for batch effects can lead to incorrect inference and requires inefficient post-hoc quality control procedures that exclude regions that are associated with batch. Our work extends previous model-based approaches for copy number estimation by explicitly modeling batch effects and using shrinkage to improve locus-specific estimates of copy number uncertainty. Key features of this approach include the use of diallelic genotype calls from experimental data to estimate batch- and locus-specific parameters of background and signal without the requirement of training data. We illustrate these ideas using a study of bipolar disease and a study of chromosome 21 trisomy. The former has batch effects that dominate much of the observed variation in quantile-normalized intensities, while the latter illustrates the robustness of our approach to datasets where as many as 25% of the samples have altered copy number. Locus-specific estimates of copy number can be plotted on the copy-number scale to investigate mosaicism and guide the choice of appropriate downstream approaches for smoothing the copy number as a function of physical position. The software is open source and implemented in the R package CRLMM available at Bioconductor (http:www.bioconductor.org).

Multilevel Latent Class Models with Dirichlet Mixing Distribution

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the Obsessive Compulsive Disorder study. Our models' random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for latent class analysis of multilevel data.

The enduring effects of psychodynamic treatments vis-à-vis alternative treatments: A multilevel longitudinal meta-analysis

Although evidence suggests that the benefits of psychodynamic treatments are sustained over time, presently it is unclear whether these sustained benefits are superior to non-psychodynamic treatments. Additionally, the extant literature comparing the sustained benefits of psychodynamic treatments compared to alternative treatments is limited with methodological shortcomings. The purpose of the current study was to conduct a rigorous test of the growth of the benefits of psychodynamic treatments relative to alternative treatments across distinct domains of change (i.e., all outcome measures, targeted outcome measures, non-targeted outcome measures, and personality outcome measures). To do so, the study employed strict inclusion criteria to identify randomized clinical trials that directly compared at least one bona fide psychodynamic treatment and one bona fide non-psychodynamic treatment. Hierarchical linear modeling (Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2011) was used to longitudinally model the impact of psychodynamic treatments compared to non-psychodynamic treatments at post-treatment and to compare the growth (i.e., slope) of effects beyond treatment completion. Findings from the present meta-analysis indicated that psychodynamic treatments and non-psychodynamic treatments were equally efficacious at post-treatment and at follow-up for combined outcomes (k=20), targeted outcomes (k=19), non-targeted outcomes (k=17), and personality outcomes (k=6). Clinical implications, directions for future research, and limitations are discussed.