331 research outputs found
Langzeiterfahrungen mit der Knietotalendoprothese nach Blauth - Stellenwert achsgeführter Kniegelenkendoprothesen
No full textAim: Currently there is widespread use of unconstrained knee arthroplasty even in severe deformities of the knee. In order to ascertain the value and future of constrained knee prostheses, we have assessed the results of the Blauth knee prosthesis and compared them with the results of unconstrained knee prostheses. Methods: In a retrospective analysis we examined 227 Blauth knee prosthesis implantations performed between 1985 and 1997. Using endpoints of prosthesis removal, infection and aseptic loosening, the 10-year survivorship analysis was evaluated. Results: The analysis shows a 10-year survivorship of 90.1% using aseptic loosening or infection as an endpoint and 96.2% using removal as an endpoint. Conclusion: The first designs of constrained knee prostheses were associated with a high failure rate, while more recent designs like the St. Georg knee prosthesis and the Blauth knee prosthesis show long-term results similar to condylar designs. Since there has been a large increase of knee endoprosthesies implantations in the last years, a constrained knee prosthesis with excellent long-term results remains a valuable implant in cases of revisions and difficult knee arthroplasty
Talk data to me: Bolstering the communication of data to facilitate data-informed decision making in community colleges
No full textCommunity colleges are continually being faced with pressures to use data to inform decisions. These pressures arise from a triage of factors, including accountability, accreditation, and student success initiatives. Yet, as these demands continue, research has shown that community colleges struggle to institutionalize data-informed decision making (DIDM) to support student success. In fact, in a 2011 survey of college and university presidents by Inside Higher Ed, only 36.1% of the 344 public community college presidents believed their college was very effective in using data to inform decisions (Green, Jaschik, & Lederman, 2011, p. 19). Through the literature review process, it became evident that open channels of communication and discussions related to data and student success are essential for DIDM (Altose, 2017; Coburn & Turner, 2011; Katz & Ain Dack, 2014; Kerrigan, 2015; McClenney, McClenney, & Peterson, 2007; Peterson, 2007), yet research exploring how these processes take place in community colleges is lacking. As such, this multiple-case study was intended to develop best practices for communicating data related to student success by exploring the communication and presentation of data through the lens of stakeholder and knowledge management theories. Two community colleges were selected based on recommendations from the CEO of Achieving the Dream who affirmed these institutions’ demonstrated efforts in supporting student success through DIDM.
Findings showed that executive leadership, administrators, and faculty are the most commonly cited stakeholders in the decision-making process related to student success. Although frequent communication of data exists in both colleges, it was apparent that frequency depends on the stakeholder group. The main method of communicating data occurs in-person. In-person communication can support accurate interpretation of data and the transition of data into information. While participants identified Institutional Research (IR) as the main area helping them to interpret data, in-person conversations with colleagues facilitate bringing meaning and context to the data that are under review. Data are often presented to internal stakeholders in the form of graphs, charts, and tables; however, there was no overall consensus on which presentation is more effective.Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed
Decision Making in Community Colleges
Talk Data to Me: Bolstering the Communication of Data to Facilitate
Data-Informed Decision Making in Community Colleges
Alexa M. Beshara-Blauth
A Thesis submitted to
the Graduate Faculty
of the
University of Maryland University College
in partial Fulfillment of
the Requirements for the
Doctor of Management Degree
Charlene Nunley, Ph.D.
Susan McMaster, DM
Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed
Decision Making in Community Colleges
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Abstract
Community colleges are continually being faced with pressures to use data to inform decisions. These pressures arise from a triage of factors, including accountability, accreditation, and student success initiatives. Yet, as these demands continue, research has shown that community colleges struggle to institutionalize data-informed decision making (DIDM) to support student success. In fact, in a 2011 survey of college and university presidents by Inside Higher Ed, only 36.1% of the 344 public community college presidents believed their college was very effective in using data to inform decisions (Green, Jaschik, & Lederman, 2011, p. 19). Through the literature review process, it became evident that open channels of communication and discussions related to data and student success are essential for DIDM (Altose, 2017; Coburn & Turner, 2011; Katz & Ain Dack, 2014; Kerrigan, 2015; McClenney, McClenney, & Peterson, 2007; Peterson, 2007), yet research exploring how these processes take place in community colleges is lacking. As such, this multiple-case study was intended to develop best practices for communicating data related to student success by exploring the communication and presentation of data through the lens of stakeholder and knowledge management theories. Two community colleges were selected based on recommendations from the CEO of Achieving the Dream who affirmed these institutions’ demonstrated efforts in supporting student success through DIDM.
Findings showed that executive leadership, administrators, and faculty are the most commonly cited stakeholders in the decision-making process related to student success. Although frequent communication of data exists in both colleges, it was apparent that frequency depends on the stakeholder group. The main method of communicating data occurs in-person. In-person communication can support accurate interpretation of data and the transition of data Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed
Decision Making in Community Colleges
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into information. While participants identified Institutional Research (IR) as the main area helping them to interpret data, in-person conversations with colleagues facilitate bringing meaning and context to the data that are under review. Data are often presented to internal stakeholders in the form of graphs, charts, and tables; however, there was no overall consensus on which presentation is more effective.
Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed
Decision Making in Community Colleges
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© Copyright by
Alexa M. Beshara-Blauth
2018 Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed
Decision Making in Community Colleges
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Dedication
My work over the past 3 years and this final dissertation are dedicated to my family; you have provided me with unwavering support and inspiration and believed in me during my most difficult times. Thank you for listening to paper after paper and taking this journey alongside me. To my dad, who instilled in me my love for math and numbers, who taught me perseverance, and who continually pushed me to start working on my doctoral degree. Thank you for not only pushing and nudging me to do what you knew I could, but also for making sure that I was well fed during the past year!
To my mom, who has been my rock and so much more throughout this entire process. You have taught me so much and I know that the most important lessons have come not from my books but from watching you. A true role model, I have learned what it means to have dedication, to stand up for what I believe in, and how to put others first. These are all traits that I will carry with me as I move on to my next chapter.
To my husband, I don’t quite think you knew what you were getting into when I signed up for this program. For most of our marriage, I have had my nose stuck behind books or was ferociously typing away at my computer. Thank you for loving me, standing by my side, and taking on more responsibilities so that I could focus my time on school. I promise I won’t start on anything crazy within the next few months, so we can truly enjoy our time together.
And most importantly, to my son, my heart. You were my motivation even before you were born. I hope that I can be as good of a role model to you as my mother has been to me. I hope that I make you proud, and that when you look back at my journey through this, you remember that anything is possible if you set your mind to it and believe in yourself. Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed
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Acknowledgements
The journey through the UMUC DMCCPA program has been one of the most trying yet rewarding experiences I have encountered. I have grown as both a student and leader, pushing myself far beyond what I thought was possible, and I owe that to the dedicated faculty of this program in addition to my cohort. I want to first acknowledge my amazing advisors, Dr. Charlene Nunley and Dr. Susan McMaster. Your patience, feedback, and guidance as I stumbled through this process have allowed me to produce a meaningful addition to community college research. Without your expertise and support, I do not believe I would have gotten through my primary research “on time.” I am forever grateful. Dr. Nunley, your dedication to student success is admirable; it is contagious and invigorating to those who encounter it. It makes me want to be a better leader and it will continue to push me as I pursue my goals. Dr. McMaster, you have such a way with words, even turning dissertation instructions into elaborate stories. Your words of encouragement were powerful and provided me with the reassurance I needed. I am very thankful for all of the grammatical edits you provided.
There are so many others from the DMCCPA program whom I wish to acknowledge. Dr. Pat Keir, your instruction for our first class made me realize I had made the right choice in selecting this program. Dr. Ronald Head, I owe my attention to APA format to you. Dr. Gena Glickman, thank you for being a willing participant for some of my research. Monica Graham, I am appreciative of your assistance throughout this program; you were always willing to answer my questions and provided any support that you could.
In addition to those in the DMCCPA program, I would like to thank Dr. Karen Stout for her willingness to discuss my dissertation and provide feedback on case study institutions. Your Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed
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assistance in identifying and reaching out to potential participants is truly appreciated.
I want to also acknowledge Dr. Paula Pitcher, my mentor and friend who recommended this program to me. Your guidance as I first began my career in higher education has been invaluable. I continue to admire your drive and ambition and look forward to seeing you attain your goals. Last, and certainly not least, I want to acknowledge my cohort. Each and every one of you has motivated me beyond imagination. Your constant support, humor, and commitment to completion kept me sane. I can’t wait to see where this journey takes everyone next!
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Table of Contents
Chapter 1 – BACKGROUND ...................................................................................................... 1
Statement of Problem ....................................................................................................... 3
Significance..................................................................................................................... 6
Funding ................................................................................................................ 9
Graduation Rates and Student Success ................................................................ 10
Communication to Facilitate Data Use ................................................................ 13
Purpose ............................................................................................................................. 14
Research Questions .......................................................................................................... 15
Theoretical Context .......................................................................................................... 15
Stakeholder Theory .............................................................................................. 16
Knowledge Management ..................................................................................... 17
Definition of Terms.......................................................................................................... 19
Summary .......................................................................................................................... 21
Chapter 2 – METHODOLOGY ................................................................................................... 23
Research Methodology Selection and Literature Evaluation ........................................... 24
Systematic Review ............................................................................................... 24
Literature Scoping ................................................................................................ 25
Key Sources ......................................................................................................... 27
Multiple-Case Study Methodology .................................................................................. 38
Case Study Site Selection .................................................................................... 39
Interview Guide Development ............................................................................. 41
Pilot ...................................................................................................................... 42
Data Analysis ....................................................................................................... 43
Methodology of Expert Panel Selection .......................................................................... 45
Summary .......................................................................................................................... 48
Chapter 3 – LITERATURE REVIEW ......................................................................................... 50
Accountability, Accreditation, Student Success, and DIDM ........................................... 51
Accountability and DIDM ................................................................................... 51
Accreditation ........................................................................................................ 53
Student Success and the Completion Agenda ...................................................... 55
Historical trends in student success ........................................................... 56
DIDM and student success ........................................................................ 57
Current status of student success ............................................................... 58
Prevalence of Data Use .................................................................................................... 59
Challenges, Barriers, and Influences in DIDM ................................................................ 63
Relevance of Data ................................................................................................ 63
Accessibility and Presentation of Data ................................................................ 65
Trust in Data and DIDM ...................................................................................... 69
Leadership Impact on DIDM ............................................................................... 71
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Chapter 3 (continued)
Communicating Data: The Importance of Channels for Communication and Collaboration ...................................................................................... 73
Institutional Research and DIDM .................................................................................... 78
Function and Size ................................................................................................. 78
Consumers of IR Data .......................................................................................... 78
Structure and Future of IR ................................................................................... 80
The Application of Stakeholder Theory .......................................................................... 82
The Origin of Stakeholder Theory ....................................................................... 83
Identification and Prioritization of Stakeholders ................................................. 84
The Importance of Understanding Stakeholders and DIDM ............................... 87
Knowledge Management Theory ..................................................................................... 88
Concepts of Knowledge Management ................................................................. 89
Connections Between Data, Information, and Knowledge .................................. 91
Knowledge Creation and Sharing ........................................................................ 92
Conceptual Model ............................................................................................................ 95
Elements of the Conceptual Model ...................................................................... 89
Accountability ........................................................................................... 96
Accreditation ............................................................................................. 98
Student success initiative ........................................................................... 98
Pressures on community colleges to use data to inform decisions ............ 99
Community colleges and DIDM ...............................................................100
Stakeholder theory and DIDM ..................................................................100
Data presentation .......................................................................................101
Communication .........................................................................................102
Transformation of data into information ...................................................102
New knowledge created ............................................................................102
Decision making ........................................................................................103
Managing knowledge ................................................................................103
Summary of Conceptual Model ...........................................................................104
Literature Review Summary ............................................................................................104
Chapter 4 – FINDINGS ...............................................................................................................106
Expert Panel Review ........................................................................................................107
Problem Statement and Significance of Problem ................................................109
Relevance of Theories..........................................................................................110
Scope of Research Questions ...............................................................................111
Organization .........................................................................................................113
Quality of Writing ................................................................................................113
Adequacy of References ......................................................................................113
Additional Expert Feedback ................................................................................114
Summary ..............................................................................................................114
Description of Case Study Sites: College A and College B ............................................115
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Chapter 4 (continued)
College A .............................................................................................................115
College A’s definition of student success .................................................116
College B .............................................................................................................116
College B’s definition of student success ..................................................117
Interview Guide (IG) Responses and Analysis of Research Questions ...........................118
Overview ..............................................................................................................118
Research Question 1 ............................................................................................119
IG Question 1 (IG1) ..................................................................................119
College A ..........................................................................................119
College B ..........................................................................................120
IG Question 2 (IG2) ..................................................................................121
College A ..........................................................................................121
College B ..........................................................................................121
IG Question 3 (IG3) ..................................................................................122
College A ..........................................................................................122
College B ..........................................................................................123
IG Question 4 (IG4) ..................................................................................123
College A ..........................................................................................123
College B ..........................................................................................124
IG Question 5 (IG5) ...................................................................
Fast-resorbable antibiotic-loaded hydrogel coating to reduce post-surgical infection after internal osteosynthesis : a multicenter randomized controlled trial
No full textBACKGROUND:
Infection is one of the main reasons for failure of orthopedic implants. Antibacterial coatings may prevent bacterial adhesion and biofilm formation, according to various preclinical studies. The aim of the present study is to report the first clinical trial on an antibiotic-loaded fast-resorbable hydrogel coating (Defensive Antibacterial Coating, DAC®) to prevent surgical site infection, in patients undergoing internal osteosynthesis for closed fractures.
MATERIALS AND METHODS:
In this multicenter randomized controlled prospective study, a total of 256 patients in five European orthopedic centers who were scheduled to receive osteosynthesis for a closed fracture, were randomly assigned to receive antibiotic-loaded DAC or to a control group (without coating). Pre- and postoperative assessment of laboratory tests, wound healing, clinical scores and X-rays were performed at fixed time intervals.
RESULTS:
Overall, 253 patients were available with a mean follow-up of 18.1 ± 4.5 months (range 12-30). On average, wound healing, clinical scores, laboratory tests and radiographic findings did not show any significant difference between the two groups. Six surgical site infections (4.6%) were observed in the control group compared to none in the treated group (P < 0.03). No local or systemic side-effects related to the DAC hydrogel product were observed and no detectable interference with bone healing was noted.
CONCLUSIONS:
The use of a fast-resorbable antibiotic-loaded hydrogel implant coating provides a reduced rate of post-surgical site infections after internal osteosynthesis for closed fractures, without any detectable adverse event or side-effects.
LEVEL OF EVIDENCE:
2
Perda dental e sua associação com a obesidade em uma população adulta do Brasil
No full textA obesidade e a perda dental são importantes problemas de saúde pública mundial e ambas as condições ocasionam desfechos adversos à saúde. Em estudos prévios, a existência de associação entre essas condições tem sido observada; no entanto, evidências ainda são inconclusivas, embora muitos estudos indicam que fatores de risco comuns estão relacionados com ambos os problemas. O objetivo principal foi avaliar a associação entre o índice de massa corporal e a perda dental em uma população adulta. O método utilizado baseou-se em dados transversais que foram coletados através de questionários autopreenchíveis e medidas antropométricas foram aferidas em 3.930 funcionários tecnicoadministrativos de uma universidade no Rio de Janeiro, participantes da Fase 1 (1999) do Estudo Pró-Saúde. Perda dental autorreferida (4 categorias) foi o desfecho de interesse e obesidade foi a variável de exposição principal. Os dados sobre os aspectos da dieta, acesso e utilização dos serviços de saúde, fatores socioeconômicos, hábitos e comportamentos de saúde e dados demográficos foram utilizados como covariáveis. Em comparação com aqueles com IMC< 25 kg/m², as pessoas com sobrepeso (IMC≥ 25 e <30 Kg/m²) e obesidade (IMC≥ 30 Kg/m²) apresentaram uma maior chance de perda dental, OR = 1,6 (IC 95% 1,4-1,9) e OR = 2,1 (IC 95% 1,8-2,5), respectivamente. Ajustando por potenciais fatores de confusão, os indivíduos com sobrepeso e obesidade tiveram um OR estatisticamente não significativo para perda dental, respectivamente OR=0,8 e OR=0,9. Os resultados são consistentes com a hipótese de que a associação entre obesidade e perda dental resulte de fatores de risco comuns.Obesity and tooth loss are important public health problems worldwide, and both conditions cause adverse health outcomes. In previous studies, an association between these conditions has been observed; however, evidence is still inconclusive, although many studies indicate that common risk factors are related to both problems. The main objective of this study was to evaluate the association between overweight/obesity and tooth loss in adults. Cross-sectional data was collected through and self-administered questionnaires and anthropometric measurements in 3,930 civil servants at a university in Rio de Janeiro, participants in Phase 1 (1999) of the Pró-Saúde Study. Self-reported tooth loss (4 categories) was the outcome of interest, and obesity was the main independent variable. Data on aspects of diet, access and utilization of health services, socioeconomic factors, health habits and behaviors as well as demographic data were used as covariates. Compared with those with BMI< 25 kg/m², overweight people (BMI≥ 25 and <30 kg/m²) and obesity (BMI≥ 30 kg/m²) had a greater chance of tooth loss, OR=1.6 (95% CI 1.4-1.9) and OR=2.1 (95% CI 1.8-2.5), respectively. Adjusting for potential confounding factors, overweight and obese participants showed no statistically significant higher odds for tooth loss, respectively, OR=0.8 and OR=0.9.The results are consistent with the hypothesis that the association between obesity and tooth loss is the result of common risk factors
Astrha : um ambiente gráfico, dinâmico e interativo para internet baseado em hiper-animações e na teoria dos autômatos
No full textEsta pesquisa, batizada Astrha (Automata Structured Hyper-Animation), tem suas raízes no projeto “Hyper Seed - Framework, Ferramentas e Métodos para Sistemas Hipermídia voltados para EAD via WWW” que possui, entre seus objetivos e metas: (a) o desenvolvimento de uma fundamentação matemática para a unificação, de maneira coerente e matematicamente rigorosa, de especificações de sistemas hipermídia e animações baseadas na Teoria dos Autômatos; (b) a construção e validação de um protótipo de sistema com suporte à criação de conteúdo multimídia e hipermídia com ênfase em educação assistida por computador; (c) a definição e aplicação de estudos de caso. Atender às demandas acadêmicas e construtoras supra citadas, no que se refere à unificação de especificações de sistemas hipermídia e animações baseadas na Teoria dos Autômatos, em nível conceitual, é o objetivo principal do Astrha. Mais especificamente, unificar conceitos das especificações Hyper-Automaton; Hyper- Automaton: Avaliações Interativas; eXtensible Hyper-Automaton (XHA) e Animação Bidimensional para World Wide Web (AGA). Para resolvê-las, propõe uma solução em cinco fases. A primeira constitui-se numa investigação conceitual sobre unificação de ambientes hipermídia com animações por computador, da qual conclui-se que as hiperanimações são uma resposta adequada ao contexto. Em seguida, um autômato finito não-determinístico, reflexivo, com saídas associadas às transições, denominado Astrha/M, é especializado para modelar, formalmente, estruturas hiper-animadas. Na terceira fase, uma linguagem de quarta geração denominada Astrha/L é proposta com a finalidade de proporcionar semântica à ambientes hiper-animados. Construída a partir da metalinguagem XML, é composta de quatro dialetos: (1) Mealy, que traduz o modelo Astrha/M; (2) Environment, que oferece opções de configuração e documentação; (3) Hyper, linguagem hipermídia, de sintaxe simples, que oferece hiperligações estendidas; (4) Style, especificação de estilos em cascata e de caracteres especiais. A quarta fase é a modelagem e construção do protótipo, denominado Astrha/E, através das linguagens UML e Java, respectivamente, com uso de tecnologias de software livre, resultando em um applet interativo, dinâmico, multimídia, que oferece características e propriedades de uma hiper-animação, traduzindo não-determinismos em escolhas pseudo-aleatórias e reflexividades em inoperabilidades aparentes. Por fim, a quinta fase trata de estudos de caso aplicados em educação a distância, em diversas áreas, de onde se conclui sua validade como conceito, modelo e ferramenta para programas educacionais que utilizam a Internet como meio de auxílio ao aprendizado
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