Protocol for a mixed methods realist evaluation of a health service user feedback system in Bangladesh

Introduction: Responsiveness to service users’ views is a widely-recognised objective of health systems. A key component of responsive health systems is effective interaction between users and service providers. Despite a growing literature on patient feedback from high-income settings, less is known about effectiveness of such systems in low and middle income countries. Methodology and analysis: This paper disseminates the protocol for an 18-month ‘RESPOND’ project that aims to evaluate the system of collecting and responding to user feedback in Bangladesh. This mixed-method study uses a Realist Evaluation approach to examine user feedback systems at two Upazila Health Complexes in Comilla district of Bangladesh, and comprises three steps: i) initial theory development; ii) theory validation; and iii) theory refinement and development of lessons learned. The project also utilises: i) Process evaluation to understand causal mechanisms and contexts of implementation; ii) Statistical analysis of patient feedback to clarify the nature of issues reported; iii) Social science methods to illuminate feedback processes and user and provider experiences; and iv) Health policy and systems research to clarify issues related to integration of feedback systems with quality assurance and human resource management. During data analysis, qualitative and quantitative findings will be integrated throughout to help achieve study objectives. Analysis of qualitative and quantitative data will be done using a convergent mixed methods model, involving continuous triangulation of multiple datasets to facilitate greater understanding of the context of user feedback systems including the links with relevant policies, practices and programmes. Ethics and dissemination: Ethics approvals were obtained from the University of Leeds and the Bangladesh Medical Research Council. All data collected for this study will be anonymised and identifying characteristics of respondents will not appear in a final manuscript or reports. The study findings will be presented at scientific conferences and published in peer-reviewed journals

Publication Records of Faculty Promoted to Professor: Evidence from the UK Accounting and Finance Academic Community

This study investigates the publication profiles of 140 accounting and finance faculty promoted to the senior rank of professor at UK and Irish universities during the period 1992 to 2007. On average, approximately 9 papers in Association of Business Schools (ABS) (2008)-listed journals, with 5 at the highest 3*/4* quality levels in a portfolio of 20 outputs are required for promotion to professor. Multivariate analysis provides evidence that publication requirements in terms of ABS ranked journal papers have increased over time, an effect attributed to the government research assessment exercise. There is no evidence that requirements differ for: internal versus external promotion, male versus female candidates; accounting versus finance professors, research intensity of institution peer group; or government research ranking of unit. There is also no evidence of a substitution effect in relation to increased recent publication history, quantity of non-ABS outputs or sole-authorship, all of which show a significant complementary effect. It is noted that there is very limited overlap in the UK and US publication journal sets, suggesting underlying geographically-based paradigm differences. The benchmarks provided in this study are informative in a range of decision settings: recruitment; those considering making an application for promotion to a chair and those involved in promotion panels; cross-disciplinary comparisons; and resource allocation. The evidence presented also contributes to the emerging policy debates concerning the aging demographic profile of accounting faculty, the management of academic labour and the Research Excellence Framework

Rethinking the Gains from Immigration: Theory and Evidence from the U.S.

Recent influential empirical work has emphasized the negative impact immigrants have on the wages of U.S.-born workers, arguing that immigration harms less educated American workers in particular and all U.S.-born workers in general. Because U.S. and foreign born workers belong to different skill groups that are imperfectly substitutable, one needs to articulate a production function that aggregates different types of labor (and accounts for complementarity and substitution effects) in order to calculate the various effects of immigrant labor on U.S.-born labor. We introduce such a production function, making the crucial assumption that U.S. and foreign-born workers with similar education and experience levels may nevertheless be imperfectly substitutable, and allowing for endogenous capital accumulation. This function successfully accounts for the negative impact of the relative skill levels of immigrants on the relative wages of U.S. workers. However, contrary to the findings of previous literature, overall immigration generates a large positive effect on the average wages of U.S.-born workers. We show evidence of this positive effect by estimating the impact of immigration on both average wages and housing values across U.S. metropolitan areas (1970-2000). We also reproduce this positive effect by simulating the behavior of average wages and housing prices in an open city-economy, with optimizing U.S.-born agents who respond to an inflow of foreign-born workers of the size and composition comparable to the immigration of the 1990s

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Hawaii’s “7 by 7” for School Health Education: A PowerPoint Presentation on Integrating the National Health Education Standards With Priority Content Areas for Today’s School Health Education in Grades Kindergarten Through 12

School-based health education can help young people develop the knowledge, skills, motivation, and support they need to choose health-enhancing behaviors and resist engaging in behaviors that put them at risk for health and social problems and school failure. The health of school-age youth is significantly associated with their school achievement. However, in the midst of today’s increased emphasis on school accountability in the areas of reading, writing, and mathematics, subject areas such as health education tend to receive less prominence in the school curriculum. Recalling their own lackluster school experiences related to health topics, decision makers may not realize that today’s skills-based school health curriculum involves a highly interactive and engaging approach to promoting good health and preventing the most serious health problems among youth. Health education is one important component of a coordinated school health program that includes health education, physical education, school health services, nutrition services, school counseling and psychological services, a healthy school environment, school promotion for faculty and staff, and involvement of family and community members. The purpose of this PowerPoint presentation — Healthy Keiki, Healthy Hawaii: Hawaii's "7 by 7" for School Health Education — is to educate health and education decision makers, teachers, parents, and community members on how Hawaii has integrated seven health education standards with seven priority health content areas to create an effective approach to school health education in grades kindergarten through 12. The goal of Hawaii’s “7 by 7” curriculum focus is to ensure that all of Hawaii’s keiki (children) have well-planned opportunities at school to become fit, healthy, and ready to learn

Klasifikasi Tumor Otak Menggunakan Backpropagation Neural Network

Brain tumors can be categorized into two main types, namely benign brain tumors and malignant brain tumors. Brain tumors can be detected by examining Magnetic Resonance Image (MRI) using medical devices. In this study Backpropagation Neural Network was implemented as a method to classify brain tumors. The stages carried out before classification are image processing in the form of Scaling and Contrast Stretching processes, then feature extraction using the Principal Component Analysis method. The backpropagation network architecture used is 21- 10-1 with the configuration of learning rate parameters = 0.1, maksium epoch = 1,000 epoch, and the target error (goal) = 0.0000001. The test results on the test data obtained method accuracy of 88.88%.Tumor otak dapat dikategorikan menjadi dua jenis utama, yaitu tumor otak benign dan tumor otak malignant. Tumor otak dapat dideteksi dengan cara melalui pemeriksaan Magnetic Resonance Image (MRI) yang menggunakan alat medis. Pada penelitian ini Backpropagation Neural Network diimplementasikan sebagai metode untuk mengklasifikasi tumor otak. Tahap-tahap yang dilakukan sebelum klasifikasi adalah pengolahan citra berupa proses Scaling dan Contrast Stretching, kemudian ekstraksi fitur menggunakan metode Principal Component Analysis. Arsitektur jaringan backpropagation yang digunakan adalah 21-10-1 dengan konfigurasi parameter learning rate = 0,1, maksium epoch = 1,000 epoch, dan target error (goal) = 0,0000001. Hasil pengujian pada data uji diperoleh akurasi metode sebesar 88,88%.99 HalamanSkripsi Sarjan