Front-line registered nurses (RNs) make up the workforce that directly affect the care of patients in a variety of different healthcare settings. RN job satisfaction is important because it is tied to retention, organizational commitment, workforce safety, patient safety, and cost savings. The strongest predictors have been difficult to determine because workplaces differ, numerous tools to measure satisfaction exist, the workforce is diversified by generations and work positions, and ongoing policy changes directly impact the work of the front-line RN. The strength and stability of the workforce depends on an accurate understanding of the predictors of job satisfaction for the front-line RN. The purpose of this study was to comprehensively, quantitatively examine predictors of front-line RN job satisfaction from 1980-2009 to provide overarching conclusions based on empirical evidence. Of interest was: the (1) estimation of large, moderate, and small predictor summary effect sizes; (2) assessment of predictor differences among decades (i.e., 1980s, 1990s, and 2000s); (3) identification of causes for predictor differences among studies (i.e., moderators); and (4) investigation of predictor differences between generations (i.e., Baby Boomers, Generation X, and Millennials). A non-a priori meta-analysis approach was guided by inclusion and exclusion criteria to review published and unpublished studies from 1980–2009. The search process identified 48 published and 14 unpublished studies used for analysis. Within the studies that met inclusion criteria, 27 job satisfaction predictors met inclusion for analysis. Studies were coded for Study Characteristics (e.g., Year of Publication, Country of Study) that were needed for moderator analysis. Predictors were coded for data that were necessary to calculate predictor summary effect sizes (i.e., r, n). Coding quality was maximized with a coding reliability scheme that included the primary investigator (PI) and secondary coder. A random-effects model was used iv to guide the calculation of summary effect sizes for each job satisfaction predictor. Publication bias was examined using funnel plots and Rosenthal’s Fail-safe N. An analysis of variance (ANOVA) was used to evaluate predictor differences among decades (i.e., 1980s, 1990s, and 2000s). Heterogeneity among studies was calculated (i.e., Q-statistic, I-squared, and Tausquared) to guide the need for moderator analysis. Moderator analyses were conducted to evaluate Study Characteristics as sources of predictor differences among studies, and to investigate the influence of Age (i.e., generation) on predictor effect sizes. The largest effect sizes were found for three predictors: Task Significance (r=.61), Empowerment (r=.55), and Control (r=.52). Moderate effect sizes were found for 10 predictors (e.g., Autonomy: r=.44; Stress: r=-.43), and small effect sizes were found for nine predictors (e.g., Wages: r=.23; Staffing Adequacy: r=.19). Significant heterogeneity between studies was present in all of the 27 predictor analyses. Effect size differences were not found between decades or generations. Moderator analysis found that the sources of the difference between studies remain unexplained indicating that unknown moderators are present. Findings from this study indicate that the largest predictors of job satisfaction for the front-line RN may be different than previously thought. Heterogeneity between studies and unidentified moderators indicate that there are significant differences among studies and more research is needed to identify the source(s) of these differences. The findings from this study can be used at the organizational, state, and national level to guide leaders to focus efforts of workplace improvements that are based on predictors that are most meaningful to front-line RNs (i.e., Task Requirements, Empowerment, and Control). Future research is needed to determine contemporary predictors of job satisfaction for the front-line RN, and the causes of heterogeneity between studies. The findings from the current study provide the critical synthesis needed to v guide educational and practice recommendations aimed at supporting job satisfaction of frontline RNs, thereby, maintaining this integral component of the healthcare workforce.

Despite extensive statements about the importance of possessing good interpersonal skills, little quantitative evidence has been brought forth to investigate these claims. At the same time, training in soft, or interpersonal, skills continues for organizational managers, customer service representatives, and members of formal work teams. Based on these considerations, the current research was guided by five broad questions. First, are gender and the Big Five personality variables important predictors in the use and effectiveness of interpersonal skills? Second, what is the relationship between various interpersonal skills and important personal and workplace outcomes? Third, given that training in interpersonal skills is prevalent in organizations today, does this training work? Further, and perhaps more importantly, under what conditions do these training interventions result in optimal outcomes? Lastly, does job complexity moderate the relationship between interpersonal skills and outcomes? To answer these questions, a series of meta-analytic investigations was conducted. The results of these analyses provided evidence for the existence of meaningful antecedents of interpersonal skills. In addition, relationships between interpersonal skills and outcomes were identified, with hypotheses in this area confirmed. The results of this research demonstrate the beneficial impact of interpersonal skills training for improving interpersonal skills. Finally, in line with predictions, job complexity was identified as a moderator of the relationship between interpersonal skills and outcomes. The current document concludes with recommendations both for researchers interested in furthering the science of interpersonal skills research, and for practitioners charged with improving the interpersonal skills of their workforce.

Strong social relationships consistently predict the quality of both physical and mental health. Society commonly recognizes the marriage relationship as one of the most committed, enduring relationships. While marital status alone can strongly predict health, it appears that moderating factors exist in this association. The quality of one's marriage can have a greater effect on health than marital status alone. We conducted a meta-analysis examining the strength of the influence that marital quality has on physical health, specifically indicated by mortality rates. To identify relevant articles, we searched multiple online databases (Embase, Psycinfo, Family and Society Studies Worldwide, and Academic Search Ultimate) up to May, 2021. We included studies that had a measure of marital quality linked with a measure of mortality. We identified 23 articles, with a combined total of 73,492 participants. We pre-registered this analysis with the PROSPERO International Prospective Register of Systematic Reviews Registration #CRD42020145352. Members of the research team coded each relevant article for the variables in the study. We then analyzed the data using random effects models. Results indicated that a statistically significant relationship exists between marital quality and survival across all studies reviewed, with high-quality marriage being positively associated with improved survival rates (OR = 1.265, 95% CI = 1.08 to 1.48, p = .003, n = 10, HR = 1.15, 95% CI = 1.05 to 1.26, p = .004, n = 12). Moderation analyses identified that the association tended to be much stronger when studies evaluated patients with a physical illness than when studies involved community samples with ostensibly normal levels of physical health. Thus the influence of marital quality on longevity is most pronounced when one of the spouses has poor health. These results support an increased focus on marital considerations in psychology, public health, and medical care.

Objective: to compare dietary treatments and their effectiveness in reducing seizures through a systematic review and meta-analysis of clinical trials Methods: Using the PRISMA Method, the Primo database was searched to find peer-reviewed articles where researchers compared one treatment against another. The treatments of choice present in all studies are the Ketogenic Diet and the Modified Atkins Diet. Due to the nature of the experiments, the Ketogenic Diet was considered the control, as it was coined first. Results: Of the 450 articles identified, 378 met the primary peer review threshold and 190 were classified as Open Access. Eight met eligibility and were included in this review. 443 patients across all studies were assigned either a Ketogenic Diet or a Modified Atkins Diet for a median period of 3 months. 48.17% of MAD patients had a significant decrease in seizure frequency, compared to 61.11% of patients under KD. As much as the ketogenic diet showed better results overall, there was no statistical difference in the odds of achieving a significant reduction in seizures using either diet. Significance: Trials suggest that while there was not a significant difference in this study between the ketogenic diet and the modified Atkins diet, there was a trend for ketogenic diets to be more effective than the more recent Modified Atkins diet for drug-resistant epileptics. However, the sample size needs to be increased before a more accurate determination can be made.

Genome-wide microarray technology has facilitated the systematic discovery of diagnostic biomarkers of cancers and other pathologies. However, meta-analyses of published arrays using melanoma as a test cancer has uncovered significant inconsistences that hinder advances in clinical practice. In this study a computational model for the integrated analysis of microarray datasets is proposed in order to provide a robust ranking of genes in terms of their relative significance; both genome-wide relative significance (GWRS) and genome-wide global significance (GWGS). When applied to five melanoma microarray datasets published between 2000 and 2011, a new 12-gene diagnostic biomarker signature for melanoma was defined (i.e., EGFR, FGFR2, FGFR3, IL8, PTPRF, TNC, CXCL13, COL11A1, CHP2, SHC4, PPP2R2C, and WNT4). Of these, CXCL13, COL11A1, PTPRF and SHC4 are components of the MAPK pathway and were validated by immunocyto- and immunohisto-chemistry. These proteins were found to be overexpressed in metastatic and primary melanoma cells in vitro and in melanoma tissue in situ compared to melanocytes cultured from healthy skin epidermis and normal healthy human skin. One challenge for the integrated analysis of microarray data is that the microarray data are produced using different platforms and bio-samples, e.g. including both cell line- and biopsy-based microarray datasets. In order to address these challenges, the computational model was further enhanced the stratification of datasets into either biopsy or cell line derived datasets, and via the weighting of microarray data based on quality criteria of data. The methods enhancement was applied to 14 microarray datasets of three cancers (breast, prostate, and melanoma) based on classification accuracy and on the capability to identify predictive biomarkers. Four novel measures for evaluating the capability to identify predictive biomarkers are proposed: (1) classifying independent testing data using wrapper feature selection with machine leaning, (2) assessing the number of common genes with the genes retrieved in independent testing data, (3) assessing the number of common genes with the genes retrieved in across multiple training datasets, (4) assessing the number of common genes with the genes validated in the literature. This enhancement of computational approach (i) achieved reliable classification performance across multiple datasets, (ii) recognized more significant genes into the top-ranked genes as compared to the genes detected by the independent test data, and (iii) detected more meaningful genes than were validated in previous melanoma studies in the literature.

This meta analysis looks at the results of performance anxiety related interventions with musicians. This meta analysis results from all found studies on computerized databases including National Library of Medicine's PubMed, dissertations and Theses (ProQuest), PsycINFO, and Oxford Journals Database. They range from cognitive interventions, behavioral interventions, mediation, and biofeedback. The results are primarily drawn from participants self report before and after the intervention. They were coded for length of intervention, number of participants, level of participant (students or professionals), type of intervention, self report measures used, and the effect size of the intervention. The overall effect of all 17 studies involved in the meta analysis was (Hedges g -0.627, 95% CI [-0.926, -0.384], p.000), followed by physiological interventions with an effect of (Hedges g = -0.638, [-1.111, -.164], p=.008), and purely cognitive interventions having the smallest effect size (Hedges g = -0.455, 95% CI [-0.757, -.153], p=.003).

This meta analysis looks at the results of performance anxiety related interventions with musicians. This meta analysis results from all found studies on computerized databases including National Library of Medicine's PubMed, Dissertations and Theses (ProQuest), PsycINFO, and Oxford Journals Database. They range from cognitive interventions, behavioral interventions, mediation, and biofeedback. The results are primarily drawn from participants self report before and after the intervention. They were coded for length of intervention, number of participants, level of participant (students or professionals), type of intervention, self report measures used, and the effect size of the intervention. The overall effect of all 17 studies involved in the meta analysis was (Hedges g -0.627, 95% CI [-0.926, -0.384], p<<>.000). The interventions were significantly different, with largest effect sizes in combination interventions (Hedges g = -0.813, 95% CI [-1.171, -.456], p>.000), followed by physiological interventions with an effect of (Hedges g = -0.638, [-1.111, -.164], p=.008), and purely cognitive interventions having the smallest effect size (Hedges g = -0.455, 95% CI [-0.757, -.153], p=.003).

The larynx is the mammalian organ of vocalization. Humans have a degree of control over this organ considerably beyond the abilities of other primates, most notably in our control over the larynx during speech. Although there is an abundance of research on the neural basis of speech, relatively little of this research has focused on the control of the larynx. First, I performed a meta-analysis to search for brain areas responsible for making explicit judgments about affective prosody to identify candidate premotor areas in prefrontal cortex that may also plan the affective component of affective prosody (Chapter 2). The inferior frontal gyrus pars orbitalis was the only prefrontal region preferentially engaged by affective vocalizations. Second, I used functional magnetic resonance imaging to determine whether there are discrete neural systems for producing innate-affective versus arbitrary non-affective vocalizations in the human brain, as has been predicted from non-human primate models (Chapter 3). The vocal-motor system demonstrated a lack of specialization since both types of vocalizations engaged the entire network. Third, I searched for brain areas that were preferentially engaged during vocal imitation (Chapter 4), which is a key process in vocal learning. Vocal imitation preferentially engaged a cortico-striate network similar to that predicted from avian models of vocal imitation. Finally, I performed a meta-analysis to explore the neural basis of persistent developmental stuttering (Chapter 5), a speech disorder that is associated with poor control of the laryngeal muscles. Among other brain areas, primary motor regions controlling the larynx were abnormally activated in the brains of people who stutter. Together these studies advance our knowledge of the human vocal-motor system, how it relates to that in other species, and how this system may be disrupted in persistent developmental stuttering. I discuss remaining gaps in our knowledge that will be the focus of my future research. / Dissertation / Doctor of Philosophy (PhD) / One hallmark of the human species is our ability to talk. This dissertation describes a body of research that uses modern brain imaging technology ¬to study the brain systems that underlie this ability in humans –referred to as the vocal-motor system. It then compares this system in humans to the closest equivalent in monkeys, since monkeys lack this ability but share relatively recent common ancestry with humans. It also makes comparisons with the brains of songbirds, since the ability of juvenile songbirds to learn songs may share similarities with the human ability to learn speech. Finally, it looks at the potential dysfunction of this system in the brains of people who stutter.

A diagnostic test identifies patients according to their disease status. Different meta-analytic models for diagnostic test accuracy studies have been developed to synthesize the sensitivity and specificity of the test. Because of the likely correlation between the sensitivity and specificity of a test, modeling the two parameters using a bivariate model is desirable. Historically, the logit transformation has been used to model sensitivity and specificity pairs from multiple studies as a bivariate normal. In this thesis, we propose two transformations, the arcsine square root and the Freeman-Tukey double arcsine transformation, in the context of a bivariate random-effects model to meta-analyze diagnostic test accuracy studies. We evaluated the performance of the three transformations (the commonly used logit and the proposed transformations) using an extensive simulation study in terms of bias, root mean square error and coverage probability. We illustrate the methods using three real data sets. The simulation study results showed that, for smaller sample size and higher values of sensitivity and specificity, the proposed transformations are less biased, have smaller root mean square error and better coverage probability than the standard logit transformation regardless of the number of studies. On the other hand, for large sample sizes, the usual logit transformation is less biased and has better coverage probability regardless of the true values of sensitivity, specificity and number of studies. However, when the sample size is large, the logit transformation has better root mean square error for moderate and large number of studies. The point estimates of the two parameters, sensitivity & specificity, from the methods using the three real data sets follow patterns similar to those reported by our simulation. / Thesis / Master of Science (MSc)

Background and Objectives: Mesothelin, identified as a tumor-associated biomarker, is more often overexpressed in triple receptor-negative breast cancer (TNBC) than in common luminal breast tumor subtype or normal tissues. The objective of this systematic review is to determine the association between the expressions of mesothelin with survival outcomes in patients with TNBC. Methods We searched the following electronic databases: MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, PubMed, and Web of Science with no time or language restriction till May 19, 2016. Any prospective or retrospective longitudinal studies that investigate the prognosis of TNBC with mesothelin baseline measurement were selected. Two reviewers independently assessed every article for inclusion, extracted data, and assessed the methodological quality of every eligible trial. Pooled measures of associations were summarized with meta-analyses. Results and conclusions Among the 592 patients with TNBC included in the four eligible studies, 269 patients (45.4%) demonstrated mesothelin expression. For the primary outcome OS, we found the trend toward decreased survival for patients with mesothelin-positive TNBC than those without mesothelin expression. We also found that for long-term OS, the association was statistically significant (OR = 0.46; 95% CI= 0.30 to 0.73; P< 0.001). For the secondary outcomes, we found that mesothelin expression in patients with TNBC was associated with lower DFS and higher overall mortality than those without mesothelin expression. Despite the limitations of sample size, this present study shows a significant association between mesothelin expressions and survival outcomes in patients with TNBC. Patients with mesothelin-positive TNBC could benefit from mesothelin-targeted immunotherapies recently in the development. / Thesis / Master of Science (MSc) / It is unclear whether mesothelin expression in triple-negative breast cancer (TNBC) is an independent prognostic marker for survival. To the best of our knowledge, no systematic review or meta-analysis has ever been done on this topic. The present systematic review aims to evaluate the role of mesothelin as a prognostic marker for TNBC. The primary objective of this review is to synthesize available evidence on the association between the expression of mesothelin and overall survival (OS) of patients with TNBC. The secondary objectives include determining the relationship between the expression of mesothelin and disease-free survival (DFS), distant metastases, and mortality. Despite some limitations, this study shows a significant association between mesothelin expressions and long-term OS rate as well as DFS rate and mortality rate in patients with TNBC. Mesothelin has a prognostic significance for patients with mesothelin based on our findings. Patients with mesothelin-positive TNBC could benefit from mesothelin-targeted immunotherapies in development.