It has been shown that the use of Health Information Technology (HIT) is associated with reduced cost and increased quality of care. This dissertation examined the use of registries in Patient Centered Medical Home (PCMH) practices. A survey questionnaire was sent to a nationwide group of clinics certified for being a PCMH. They were asked to provide information about their payer mix, implementation barriers, registry implementation, registry use, and clinic satisfaction. The survey instrument was validated by an expert panel which included practitioners and researchers. Statistical methods including Structural Equation Modeling were used for analysis and to test the research hypotheses. The majority of medical home practices that responded used some type of computerized registry, either with basic patient information or integrated with detailed clinical information. And on average, they somewhat used registries for population management, individual health management, proactive care and planned care visits. All practices encountered some combination of barriers when implementing a medical home program. Most practices reported clinic satisfaction at least improved after becoming a medical home. The results of the analysis show that indeed payer mix, in particular Medicare and private insurance, has a significant relationship with level of registry implementation. There were no significant relationships between barriers and registry implementation or use. More sophisticated registry implementation led to greater registry use. And registry use is associated with increased clinic satisfaction. This research fills an important gap in understanding Health IT use, registries in particular, among Patient-Centered Medical Homes. The findings suggest that: 1) Implementation barriers may not be influencing use of computerized registries in medical home practices; 2) Using more sophisticated computerized registries facilitates registry use, which can help improve clinic satisfaction; 3) Payer mix may influence use of more sophisticated Health IT in medical home practices.

No description available.

Transdisciplinary research in general, and stress research in particular, requires an efficient integration of methodological knowledge of all involved academic disciplines, in order to obtain conclusions of incremental value about the investigated constructs. From a psychologist’s point of view, biochemistry and quantitative neuroendocrinology are of particular importance for the investigation of endocrine stress systems (i.e., the HPA axis, and the SNS). Despite of their fundamental role for the adequate assessment of endocrine activity, both topics are rarely covered by conventional psychological curriculae. Consequently, the transfer of the respective knowledge has to rely on other, less efficient channels of scientific exchange. The present thesis sets out to contribute to this exchange, by highlighting methodological issues that are repeatedly encountered in research on stress-related endocrine activity, and providing solutions to these issues. As outlined within this thesis, modern stress research tends to fall short of an adequate quantification of the kinetics and dynamics of bioactive cortisol. Cortisol has gained considerable popularity during the last decades, as its bioactive fraction is supposed to be reliably determinable from saliva and is therefore the most conveniently obtainable marker of HPA activity. However, a substantial fraction of salivary cortisol is metabolized to its inactivated form cortisone by the enzyme 11β-HSD2 in the parotid glands, which is likely to restrict its utility. Although the commonly used antibody-based quantification methods (i.e. immunoassays) might “involuntarily” qualify this issue to some degree (due to their inherent cross-reactivity with matrix components that are structurally-related to cortisol; e.g., cortisone), they also cause differential within-immunoassay measurement bias: Salivary cortisone has (as compared to salivary cortisol) a substantially longer half-life, which leads to an overestimation of cortisol levels the more time has passed since the onset of the prior HPA secretory episode, and thus tends to distort any inference on the kinetics of bioactive cortisol. Furthermore, absolute cortisol levels also depend on the between-immunoassay variation of antibodies. Consequently, raw signal comparisons between laboratories and studies, which are favorable as compared to effect comparisons, can hardly be performed. This finding also highlights the need for the long-sought standardization of biochemical measurement procedures. The presumably only way to circumvent both issues is to rely on quantification of ultrafiltrated blood cortisol by mass-spectrometric methods. Being partly related to biochemical considerations with research on HPA activity, a second topic arises concerning the operationalization of the construct itself: In contrast to the simple outcome measures like averaged reaction times, inclined stress researchers can only indirectly infer on the sub-processes being involved in HPA activity from longitudinally sampled hormone concentrations. HPA activity can be quantified either by (a) discrete-time, or by (b) continuous-time models. Although the former is the most popular and more convenient approach (as indicated by the overly frequent encounter of ANOVAs and trapezoidal AUC calculations in the field of psychobiological stress research), most discrete time models form rather data-driven, descriptive approaches to quantify HPA activity, that assume the existence of some endocrine resting-state (i.e., a baseline) at the first sampling point and disregard any mechanistic hormonal change occurring in between all following sampling points. Even if one ignores the fact, that such properties are unlikely to pertain to endocrine systems in general, many generic discrete time models fail to account for the specific structure of endocrine data that results from biochemical hormone measurement, as well as from the dynamics of the investigated system. More precisely speaking, cortisol time series violate homoscedasticity, residual normality, and sphericity, which need to be present in order to enable (mixed effects) GLM-based analyses. Neglecting these prerequisites may lead to inference bias unless counter-measures are taken. Such counter-measures usually involve alteration of the scale of hormone concentrations via transformation techniques. As such, a fourth-root transformation of salivary cortisol (being determined by a widely used, commercially available immunoassay) is shown to yield the optimal tradeoff for generating homoscedasticity and residual normality simultaneously. Although the violation of sphericity could be partly accounted for by several correction techniques, many modern software packages for structural equation modeling (e.g., Mplus, OpenMX, Lavaan) also offer the opportunity to easily specify more appropriate moment structures via path notation and therefore to relax the modeling assumptions of GLM approaches to the analysis of longitudinal hormone data. Proceeding from this reasoning, this thesis illustrates how one can additionally incorporate hypotheses about HPA functioning, and thus model all relevant sub-processes that give rise to HPA kinetics and dynamics. The ALT modeling framework being advocated within this thesis, is shown to serve well for this purpose: ALT modeling can recover HPA activity parameters, which are directly interpretable within a physiological framework, that is, distinct growth factors representing the amount of secreted cortisol and velocity of cortisol elimination can serve to interpret HPA reactivity and regulation in a more unambiguous way, as compared to GLM effect measures. For illustration of these advantages on a content level, cortisol elimination after stress induction was found to be elevated as compared to its known pharmacokinetics. While the mechanism behind this effect requires further investigation, its detection would obviously have been more difficult upon application of conventional GLM methods. Further extension of the ALT framework allowed to address a methodological question, which had previously been dealt with by a mere rule of thumb; what’s the optimal threshold criterion, that enables a convenient but comparably accurate classification of individuals whose HPA axis is or is not activated upon encountering a stressful situation? While a rather arbitrarily chosen baseline-to-peak threshold of 2.5 nmol/L was commonly used to identify episodes of secretory HPA activity in time series of salivary cortisol concentrations, a reanalysis of a TSST meta- dataset by means of ALT mixture modeling suggested that this 2.5 nmol/L criterion is overly conservative with modern biochemical measurement tools and should be lowered according to the precision of the utilized assay (i.e., 1.5 nmol/L). In sum, parametric ALT modeling of endocrine activity can provide a convenient alternative to the commonly utilized GLM-based approaches that enables the inference on and quantification of distinct HPA components on a theoretical foundation, and thus to bridge the gap between discrete- and continuous-time modeling frameworks. The implementation of the outlined modeling approaches by the respective statistical syntaxes and practical guidelines being derived from the comparison of cortisol assays mentioned above, are provided in the appendix of the present thesis, which will hopefully help stress researchers to directly quantify the construct they actually intend to assess.:1. Introduction 2. The hypothalamus-pituitary-adrenal (HPA) axis 3. Induction and quantification of HPA activity 4. The pitfalls of SCC measurement 5. Creating normality and homoscedasticity: GLM-based analyses 6. Relaxing sphericity: moment structure analyses 7. General conclusion

Data quality has become an area of increasing concern in marketing research. Methods of collecting data, types of data analyzed, and data analytics techniques have changed substantially in recent years. It is important, therefore, to examine the current state of marketing research, and particularly self-administered questionnaires. This paper provides researchers important advice and rules of thumb for crafting high quality research in light of the contemporary changes occuring in modern marketing data collection practices. This is accomplished by a proposed six-step research design process that ensures data quality, and ultimately research integrity, are established and maintained throughout the research process—from the earliest conceptualization and design phases, through data collection, and ultimately the reporting of results. This paper provides a framework, which if followed, will result in reduced headaches for researchers and more robust results for decision makers.

The present empirical study examined factors that affect the acceptance and use of social media platforms by farmers and vendors in farmers’ communities in North Mississippi for marketing their small farm businesses. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), structural equations modeling was used to examine several relationships: (1) the influence of performance expectancy (PE) on behavioral intention (BI) to use social media; (2) the influence of effort expectancy (EE) on BI; (3) the influence of social influence (SI) on BI; (4) the influence of facilitating conditions (FC) on actual use (USE) of social media; (5) the moderating influence of gender on the PE–BI, EE–BI, and SI-BI relationships; (6) the moderating influence of age on the PE–BI, EE–BI, SI-BI, and FC–USE relationships; and (7) the moderating influence of experience using social media on the EE–BI, SI-BI, and FC–USE relationships. Results from 169 respondents who completed questionnaires indicated that PE, EE, SI, and FC (key constructs) did predict farmers and vendors’ BI to use social media and actual USE of social media for marketing their small farm businesses and agriproducts. Respondents’ characteristics (e.g., age, gender, social media experience) did moderate some of these relationships in different ways. Thus, the present study provided additional empirical support for UTAUT. Additionally, responses to questions that assessed constructs in UTAUT suggest that respondents are open to social media as a marketing tool for their small farm businesses. Other results indicated that farmers and vendors prefer to market their products through farmers markets and community supported agriculture groups as compared to retail outlets and noted barriers present in retail marketing channels. This study provides information that will be beneficial for the development of educational programs and contributes to the literature on the factors affecting farmers’ intention to use social media to promote agriproducts to connect new markets.

No description available.

This research evaluates the technological, organizational, and environmental determinants of emerging technologies adoption represented by Artificial Intelligence (AI) and Internet of Things (IoT) driving SMEs’ competitive advantage within a resource-based view (RBV) theoretical approach supported by the technological-organizational-environmental (TOE)-framework setting. Current literature on SMEs competitive advantage as outcome of emerging technologies in the technological, organisational, and environmental contexts presents models focused on these contexts individual components. There are no models in the literature to represent the TOE framework as an integrated structure with gradual levels of complexity, allowing for incremental evaluation of the business context in support of decision making towards emerging technologies adoption supporting the firm competitive advantage. This research gap is addressed with the introduction of a new concept, the IT resource-based renewal, underpinned by the RBV, and supported by the TOE framework for providing a holistic understanding of the SMEs strategic renewal decision through information technology. This is achieved through a complex measurement model with four level constructs, leading into a parsimonious structural model that evaluates the relationships between IT resource-based renewal, and emerging technologies adoption driving SMEs competitive advantage. The model confirms the positive association between the IT resource-based renewal and emerging technologies adoption, and between the IT resource-based renewal and SME competitive advantage for the SMEs managers model, with the SME owners model outcomes are found not being supportive towards emerging technologies adoption driving SME competitive advantage. As methodology, PLS-SEM is used for its capabilities of assessing complex paths among model variables. Analysis is done on three models, one for the full sample, with two subsequent ones for owners and managers, respectively, as SME decision makers, with data collected using a web-based survey in Canada, the UK, and the US, that has provided 510 usable answers. This research has a theoretical contribution represented by the introduction of the IT resource-based renewal concept, that integrates the RBV perspective and the TOE framework for supporting organization’s decision on emerging technologies adoption driving SMEs competitive advantage. As practical implications, this thesis provides SMEs with a reference framework on adopting emerging technologies, offering SME managers and owners a comprehensive model of hierarchical factors contributing to SMEs competitive advantage acquired as outcome of AI and IoT adoption. This research makes an original contribution to the enterprise management, information systems adoption, and SME competitive advantage literature, with an empirical approach that verifies a model of emerging technologies adoption determinants driving SMEs competitive advantage.

Indiana University-Purdue University Indianapolis (IUPUI) / Symptoms of post-traumatic stress disorder (PTSD) are a common comorbidity in veterans seeking treatment of chronic musculoskeletal pain (CMP). However, little is known regarding the mutual influence of PTSD and CMP in this population. Using cross-sectional and longitudinal data from a randomized clinical trial evaluating a stepped care intervention for CMP in Iraq/Afghanistan veterans (ESCAPE), this dissertation examined the relationships between PTSD and CMP along with other factors including depression, anxiety, catastrophizing and health-related quality of life. The Classification and Regression Tree (CART) analysis was conducted to identify key factors associated with baseline PTSD besides CMP severity. A series of statistical analyses including logistical regression analysis, mixed model repeated measure analysis, confirmatory factor analysis and cross-lagged panel analysis via structural equation modeling were conducted to test five competing models of PTSD symptom clusters, and to examine the mutual influences of PTSD symptom clusters and CMP outcomes. Results showed baseline pain intensity and pain disability predicted PTSD at 9 months. And baseline PTSD predicted improvement of pain disability at 9 months. Moreover, direct relationships were found between PTSD and the disability component of CMP, and indirect relationships were found between PTSD, CMP and CMP components (intensity and disability) mediated by depression, anxiety and pain catastrophizing. Finally, the coexistence of PTSD and more severe pain was associated with worse SF-36 Physical Component Summary (PCS) and Mental Component Summary (MCS) scores. Together these findings provided empirical support for the mutual maintenance theory.

The purpose of the study was to identify and transform, as necessary, constructs of communication satisfaction and to develop a hybrid quantitative audit of organisational communication satisfaction for collectivist contexts that is both reliable and valid, using Amos Graphics for structural equation modelling. The objective was also to develop a full latent variable model and to test its fitness to the data collected from a random sample of civil servants across Addis Ababa’s civil service bureaus. The study comprised three sequential parts, namely pilot, exploratory factor analysis (EFA) (Main Study One) and confirmatory factor analysis (CFA) (Main Study Two). These were used as per the existing framework in instrument development and validation. The pilot study indicated the need for more robust data. After a series of tests, principal factor axis factoring with oblique rotation was used as the most appropriate for perceptual data, out of several options on the EFA menu. The initially hypothesised six-factor solution with the dimensions of horizontal communication, personal feedback, supervisory communication, communication climate, relational trust and job satisfaction was found to be unfit for the data on conceptual and statistical grounds and psychometric analyses which involved the use of eigenvalues and the scree plot. A more appropriate two-factor solution based on the more precise parallel analysis strategy was consistent with current research that communication satisfaction is best conceptualised in terms of informational and relational domains as operationalised using the EFA procedure. The two-factor solution led to the formation of a 17-item scale out of the original 30-item measure, with two latent dimensions namely relational satisfaction and informational satisfaction. The items of the new EFA-generated organisational communication satisfaction scale were renumbered consecutively and the scale was cross-validated on a xiv new sample of 288 civil servants from the Addis Ababa City Administration. The cross-validation necessitated model respecification and re-estimation. The respecified model underwent validation at different levels. All seven aspects of validity, namely content validity, construct validity, factorial validity, reliability, convergent validity, discriminant validity and nomological validity, were addressed and found to be adequate. However limitations are also indicated as avenues for further enquiry. / Communication Science / D. Litt. et Phil. (Communication)

本研究的主要目的是以美國全人醫治協會（American Holistic Medical Association，AHMA），提出的全人醫治取向（AHMA, 2005），發展出一份新取向的憂鬱量表，同時探討憂鬱症的相關因素。針對憂鬱症而言，全人的醫治方式需要檢視情緒、人際關係、身體和心靈等各個層面。因此，本研究所發展的自編憂鬱量表依全人醫治取向將其為分為認知取向、情緒取向、身體取向與人際取向等四個因素。 本研究量表的計分方式採用李克特式四點量表形式。原始量表共37題，依據文獻與醫院觀察所得資料編製而成。原始量表以專家效度作為刪題依據，刪題後的正式量表為22題。其計分方式，答「總是如此」得3分，「經常如此」2分，「偶爾如此」1分，「很少如此或沒有」0分。樣本分為實驗組（憂鬱症者）與效標組（非憂鬱症者），實驗組是以精神科醫師診斷為憂鬱症者共213人作為量表的填答對象，效標組是隨機抽取政大學生200人作為施測樣本，並以CES-D憂鬱量表作為效標。施測所得資料以SPSS進行描述統計、t考驗、效標關聯效度、分量表與總分之相關、區別分析、內部一致性信度的考驗；並以結構方程式模式（SEM）針對驗證性因素分析、多群組共變數結構與多群組潛在平均數結構進行考驗。 本研究的結論下： 一、本研究結果，憂鬱症好發年齡與國外25-44歲研究相符，與國內20-40歲研究結果相同。依據文獻所示，女性罹患憂鬱症的比例，為男性的2倍，本研究結果顯示與國內外研究結果是一致性。 二、各分量表與總分之間的相關從.836到.903皆為非常良好的係數值，表示本研究自編憂鬱量表具有良好的內部一致性。 三、本研究之效標關聯效度達0.939，雙尾檢定，達.01顯著水準，表示本研究所編製的量表，可測得所欲測得的特質。 四、本研究的自編量表整體內部一致性Cronbach's Alpha係數為.965，其他如刪除各單題後的總量表、校正後單題與總量表之相關、校正後單題與分量表之相關都顯示本研究自編憂鬱量表有良好的信度。 五、驗證性因素分析模式是合理適配的，表示當初四個向度假設是成立，而且這四個因素背後有一個潛在因素－「憂鬱症」存在。 六、實驗組與效標組在共變數結構上的考驗是沒有差異的，表示此自編憂鬱量表可以同時適用在實驗組與效標組。 七、分量表或總量表從t考驗值差異皆達.001顯著水準，另外區別分析Hit Ratio值為0.93，表示本研究自編之憂鬱量表具有良好之區別效度，可以有效地區別出實驗組與效標組。而在多群組潛在平均數結構上的考驗，效標組在自編憂鬱量表四個向度上的潛在平均數低於實驗組，表示本量表從潛在變項的考驗也可以有效地區辨實驗組與效標組。 / The main purpose of this study is to develop a depression scale in a new approach that reflects the whole-person approach to healing proposed by the AHMA (the American Holistic Medical Association) in the United States (AHMA, 2005), while at the same time probing into the relevant factors of depression disorders. Regarding depression disorders, a whole-person approach to healing requires the examination of multiple dimensions of the person, such as mood, interpersonal relationships, body, soul, etc. So, this study of the development of a self-establishment depression scale is divided into four dimensions: cognitive, mood, physical, and interpersonal, in accordance with the whole-person approach. The scale of this study adopts a four-item Likter scale to measure scores. The basic scale amounts to 37 items and works out according to the literature and the observing materials in hospitals. Items of the basic scale are deleted with expert validity. It is a 22- items formal scale after deleting items. Answer choices read and rate as follows: “always true” (3), “often true” (2), “true once in a while” (1), “seldom or not true” (0). Samples are separated into the experimental group (depressed person) and the criterion group (non-depressed person). In the experimental group, psychiatrists diagnosed 213 people for depressed person as the measuring target filling of the self-establishment depression scale. The criterion group consisted of 200 National Chengchi University students. CES-D was used as a criterion t. We used the program of SPSS to compute descriptive statistics, t-test, criterion-related validity, correlation of the sub-scales and the scale, discriminant, reliability of internal consistency with SPSS, and the estimations of confirmatory factory analysis, multi-sample covariance structure, multi-sample-latent-means structure with structural equation modeling (SEM). In sum, some conclusions of this research are as follows: 1. The result of this study show that depression disorder peaks during the ages of twenty-five to forty-four, consistent with in foreign and domestic research. As literature, depression disorder occurs in at double the rate in women as in men, and the result of this study is consistent with domestic and foreign studies. 2. The correlative coefficient of the sub-scales and the scale from .836 to.903 shows all as having very good coefficient value and that it has good internal consistency of the self-establishment depression scale. 3. The criterion-related validity of this research is up to 0.939, 2-tailed test reaches .01 level of significance which shows the self-establishment depression scale can examine the attributes which we want to find out. 4. The whole internal consistency Cronbach's Alpha coefficient of the self-establishment depression scale is .965, others like Cronbach's Alpha if item deleted, corrected item-total correlations single item with sub-scales and corrected item-total correlations single item with the scale all show good reliability. 5.The model confirmatory factory analysis is reasonably fit that shows four dimensions established supposing originally, and behind the back of four factors there is one latent variable – “depressive disorder” exists. 6.The estimation of covariance structure between the experimental group and the criterion group shows no difference that reveals the self-establishment depression scale can be suitable for the experimental group and the criterion group . 7.The sub-scale and the scale of the self-establishment depression scale from t-test reaches .01 level of significance, another time hit ratio value of discriminant is 0.93, both show that the self-establishment depression scale by this study can distinguish the experimental group and the criterion group efficiently. And at the estimation of multi-sample-latent-means structure shows the criterion group’s latent mean is lower than the experimental group’s, that means this scale can distinguish the experimental group and the criterion group usefully at the same time.