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Dear Student,
Pursuant to the Abraham S. Fischler College of Education (FCE) Student Grievance
Procedure, the Grievance Form is for use in filing a grievance when a satisfactory
resolution is not achieved through a formal appeal. Please note that this form and any
supporting documentation must be properly completed, received, and on file in the
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Cardinal Station Newburg Center for Primary Care
215 Central Avenue, Suite 100 1941 Bishop Lane, Suite 900 215 Central Avenue, Suite 205
Louisville, KY 40208 Louisville, KY 40218 Louisville, Ky 40208
I:\FCM\Phyllis Harris\Forms\New Patient Pkg Components
UofL Department of Family & Geriatric Medicine
Dear New Patient,
Welcome to your University of Louisville Physicians Family practice! We
are offering patient-centered medical care and are enthusiastic about our
relationships with our patients. In order to better serve your needs, we are
enclosing several forms and ask that you completely fill each form out.
The first sheet will help us learn more about you; please completely fill out this
form about your family history. The next sheet is titled, "Authorization for the
use and/or Disclosure of Protected Health Information", and you will need to
completely fill that out for our doctors to treat you to the best of their ability; it
gives us permission to review your medical records from your previous primary
medical facilities.
Following, please completely fill out the Registration, Social Services & Consent
Form. Next, you will find our Privacy Notice, followed by an acknowledgement that
you have received and understand our Privacy Policies. Finally, the last form is the
Office Acknowledgements and Policies form. Please read carefully and sign
your name at the bottom of the letter.
Please make sure to bring all of these forms with you to your first office visit.
Do not mail them back to the office. Also, please remember to always
bring your picture ID, current insurance cards and your co-payment. If your
health insurance requires you to select a primary care doctor please do so prior to
your office visit. Please bring in any and all medication you take, in their
original bottles, to your appointment.
If the patient is under 18 years of age he or she must be accompanied by an
adult and will need to bring a copy of their current immunization certificate.
Please arrive 15 minutes ahead of your scheduled appointment time so that if
you have questions about these forms or we need more information, we can
address it all prior to your appointment.
We look forward to seeing you!
University of Louisville Physicians
UofL Family and Geriatric Medicine

----SXaw2iZY;hlQeoi ----MsMykhAo;jbKoZl sufficient new science or controversy to prompt a systematic review. As a result of this prioritization, there were fewer reviews completed in 2015 (166) than in 2010 (274). Once the topics were selected, there were 2 important additions to the 2015 process of review itself. First, reviewers used Grading of Recommendations Assessment, Development, and Evaluation (GRADE; www.gradeworkinggroup.org), a highly structured and reproducible evidence review system, to improve the consistency and quality of the 2015 systematic reviews. Second, reviewers from around the world were able to work together virtually to complete the systematic reviews through the use of a purpose-built AHA Web-based platform, the Systematic Evidence Evaluation and Review System (SEERS), designed to support the many steps of the evaluation process. This SEERS site was used to provide public disclosure of drafts of the ILCOR 2015 International Consensus on CPR and ECC Science With Treatment Recommendations and to receive public comment. To learn more about SEERS and to see a comprehensive list of all systematic reviews conducted by ILCOR, visit www.ilcor.org/seers. The 2015 AHA Guidelines Update for CPR and ECC is very different from previous editions of the AHA Guidelines for CPR and ECC. The ECC Committee determined that this 2015 version would be an update, addressing only those topics addressed by the 2015 ILCOR evidence review or those requested by the training network. This decision ensures that we have only one standard for evidence evaluation, and that is the process created by ILCOR. As a result, the 2015 AHA Guidelines Update for CPR and ECC is not a comprehensive revision of the 2010 AHA Guidelines for CPR and ECC. Such an integrated version is available online at ECCguidelines.heart.org. The publication of the 2015 International Consensus on CPR and ECC Science With Treatment Recommendations begins a process of ongoing review of resuscitation science. The topics reviewed in 2015 will be updated as needed and new topics will be added. Readers will want to monitor the SEERS site to keep up-to-date on the newest resuscitation science and the ILCOR evaluation of that science. When sufficient evidence emerges that indicates the need to change the AHA Guidelines for CPR and ECC, such changes will be made and communicated to clinicians and to the training network. The 2015 Guidelines Update used the most recent version of the AHA definitions for the Classes of Recommendation and Levels of Evidence (Figure 1). Readers will note that this version contains a modified Class III recommendation, Class III: No Benefit, to be used infrequently when evidence suggests a strategy is demonstrated by a high- or moderatequality study (Level of Evidence A or B, respectively) to be no better than the control. The Levels of Evidence have also been modified. LOE B is now divided into LOE B-R (randomized studies) and LOE B-NR (nonrandomized studies). LOE C is now divided into LOE C-LD (limited data) and C-EO (expert opinion). As outlined in the recently published Institute of Medicine report4 and the AHA ECC consensus response to this report,5 more needs to be done to advance the science and practice of resuscitation. There must be a concerted effort to fund cardiac arrest resuscitation research similar to what has driven cancer and stroke research over the past 2 decades. The gaps in the science are clear when the recommendations contained within the 2015 Guidelines Update are scrutinized (Figure 2). Collectively, the Levels of Evidence and the Classes of Recommendation in resuscitation are low, with only 1% of the total recommendations in 2015 (3 of 315) based on the highest Level of Evidence (LOE A) and only 25% of the recommendations (78 of 315) designated as Class I (strong recommendation). Most (69%) of the 2015 Guidelines Update recommendations are supported by the lowest Levels of Evidence (LOE C-LD or C-EO), and nearly half (144 of 315; 45%) are categorized as Class IIb (weak recommendation). Throughout the ILCOR evidence evaluation process and the 2015 Guidelines Update development, participants adhered strictly to the AHA conflict of interest disclosure requirements. The AHA staff processed more than 1000 conflict of interest disclosures, and all Guidelines writing group chairs and at least 50% of Guidelines writing group members were required to be free of relevant conflicts of interest. ----HZaFo8jr;UWDoCS ----WlYwvINY;kwfzNK

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Dear Prospective Ed.D., Higher Education Strand Applicant:
We are very pleased that you are interested in the Higher Education Strand of CCSU's Doctor of
Education (Ed.D.) in Educational Leadership, designed for current higher education professionals
who aspire to leadership positions on college or university campuses. We look forward to receiving
your application.
As you complete your application, keep in mind the following admission criteria:
1. Master's degree from an accredited institution of higher education in a discipline or
professional field that is relevant to the Ed.D. in Educational Leadership.
2. A 3.00 or higher cumulative average (GPA) in all graduate coursework.
3. Two or more letters of reference from leaders in postsecondary education familiar with
your work. Ask your references to use the form on the next page.
4. Résumé that illustrates important work-related experiences with an emphasis on yo ur
work as a leader at postsecondary institutions of higher education.
5. Acceptable scores on the General Test of the Graduate Record Examination (GRE) taken
within five years of your application.
6. A personal statement covering six important topics:
• Career goals
• Intended area of individual specialization
• Reasons for pursuing a doctorate
• Commitment to residency requirements (one three-day weekend in the first spring
semester, one full week each of the first, second, and third summer sessions)
• Commitment to enrolling in two cohort courses each spring and fall semester
• Commitment to summer enrollment during each 8-week summer session
7. If selected as a finalist, a satisfactory interview with the admissions committee.
We accept new students in alternate years only. Applications are due by October 1, 2017.
Admission standards are rigorous, and not everyone who meets our standards wil l be accepted.
Please note that the admission process calls for submission of materials to two locations. The last
page of this packet is a checklist of the various steps. Submit your Graduate Application and $50
application fee online. Transcripts from every college you have attended as an undergraduate and
graduate student should be submitted to Graduate Admissions in 102 Barnard Hall. In addition you
must send the following materials directly to the Ed.D. Program (attention Rouzan Kheranian) in 320
Barnard Hall:
1. Two letters of recommendation from educational leaders. Use the Reference Form (page
2 of this packet).
2. Your personal statement attached to the form on page 3 of this packet.
3. Your résumé.
4. Your GRE scores. When requesting that scores be sent, use GRE reporting code 3143 to
assure that the Ed.D. office receives your scores.
Cordially,
Peter F. Troiano, Ph.D.
Ed.D. Program Direct or, Higher Education Strand

----59w9gwPg;lzDVLl RESEARCH FINDINGS There is a diversity crisis in the AI sector across gender and race. Recent studies found only 18%of authors at leading AI conferences are women,i and more than 80%of AI professors are men.ii This disparity is extreme in the AI industry:iii women comprise only 15% of AI research staff at Facebook and 10% at Google. There is no public data on trans workers or other gender minorities. For black workers, the picture is even worse. For example, only 2.5% of Google's workforce is black, while Facebook and Microsoft are each at 4%. Given decades of concern and investment to redress this imbalance, the current state of the field is alarming. The AI sector needs a profound shift in how it addresses the current diversity crisis. The AI industry needs to acknowledge the gravity of its diversity problem, and admit that existing methods have failed to contend with the uneven distribution of power, and the means by which AI can reinforce such inequality. Further, many researchers have shown that bias in AI systems reflects historical patterns of discrimination. These are two manifestations of the same problem, and they must be addressed together. The overwhelming focus on 'women in tech' is too narrow and likely to privilege white women over others. We need to acknowledge how the intersections of race, gender, and other identities and attributes shape people's experiences with AI. The vast majority of AI studies assume gender is binary, and commonly assign people as 'male' or 'female' based on physical appearance and stereotypical assumptions, erasing all other forms of gender identity. Fixing the 'pipeline' won't fix AI's diversity problems. Despite many decades of 'pipeline studies' that assess the flow of diverse job candidates from school to industry, there has been no substantial progress in diversity in the AI industry. The focus on the pipeline has not addressed deeper issues with workplace cultures, power asymmetries, harassment, exclusionary hiring practices, unfair compensation, and tokenization that are causing people to leave or avoid working in the AI sector altogether. The use of AI systems for the classification, detection, and prediction of race and gender is in urgent need of re-evaluation. The histories of 'race science' are a grim reminder that race and gender classification based on appearance is scientifically flawed and easily abused. Systems that use physical appearance as a proxy for character or interior states are deeply suspect, including AI tools that claim to detect sexuality from headshots,iv predict 'criminality' based on facial features,v or assess worker competence via 'micro-expressions.' vi Such systems are replicating patterns of racial and gender bias in ways that can deepen and justify historical inequality. The commercial deployment of these tools is cause for deep concern. i. Element AI. (2019). Global AI Talent Report 2019. Retrieved from https://jfgagne.ai/talent-2019/. ii. AI Index 2018. (2018). Artificial Intelligence Index 2018. Retrieved from http://cdn.aiindex.org/2018/AI%20Index%202018%20Annual%20Report.pdf. iii. Simonite, T. (2018). AI is the future - but where are the women? WIRED. Retrieved from https://www.wired.com/story/artificial-intelligenceresearchers-gender-imbalance/. iv. Wang, Y., & Kosinski, M. (2017). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of Personality and Social Psychology. v. Wu, X. and Zhang, X. (2016). Automated Inference on Criminality using Face Images. Retrieved from https://arxiv.org/pdf/1611.04135v2.pdf. vi. Rhue, L. (2018). Racial Influence on Automated Perceptions of Emotions. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_ id=3281765. Discriminating Systems: Gender, Race, and Power in AI | Research Findings | 3 RECOMMENDATIONS 1. Publish compensation levels, including bonuses and equity, across all roles and job categories, broken down by race and gender. 2. End pay and opportunity inequality, and set pay and benefit equity goals that include contract workers, temps, and vendors. 3. Publish harassment and discrimination transparency reports, including the number of claims over time, the types of claims submitted, and actions taken. 4. Change hiring practices to maximize diversity: include targeted recruitment beyond elite universities, ensure more equitable focus on under-represented groups, and create more pathways for contractors, temps, and vendors to become full-time employees. 5. Commit to transparency around hiring practices, especially regarding how candidates are leveled, compensated, and promoted. 6. Increase the number of people of color, women and other under-represented groups at senior leadership levels of AI companies across all departments. 7. Ensure executive incentive structures are tied to increases in hiring and retention of underrepresented groups. 8. For academic workplaces, ensure greater diversity in all spaces where AI research is conducted, including AI-related departments and conference committees. Recommendations for Addressing Bias and Discrimination in AI Systems 9. Remedying bias in AI systems is almost impossible when these systems are opaque. Transparency is essential, and begins with tracking and publicizing where AI systems are used, and for what purpose. 10. Rigorous testing should be required across the lifecycle of AI systems in sensitive domains. Pre-release trials, independent auditing, and ongoing monitoring are necessary to test for bias, discrimination, and other harms. 11. The field of research on bias and fairness needs to go beyond technical debiasing to include a wider social analysis of how AI is used in context. This necessitates including a wider range of disciplinary expertise. 12. The methods for addressing bias and discrimination in AI need to expand to include assessments of whether certain systems should be designed at all, based on a thorough risk assessment.

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