Quality Framework Research Articles

Retraction notice: The impact of quality framework application on patients’ satisfaction

Retraction notice: The impact of quality framework application on patients’ satisfaction

An approach towards total quality in architectural education in India track: higher education (architecture education)

Architecture Education is a service and product, which demands, quality delivery to meet the expectations of stakeholders. Architecture Education in India is over 100 years old, and the focus of higher education in India post-independence has been on addressing the demand for skilled professionals required for a developing nation. Over the years the emphasis on quantity has impacted the quality of graduates and there is a need for a quality framework for better delivery to all the stakeholders and to produce more employable graduates. Quality imperatives are critical to all contemporary societies which demand a better quality of life through holistic built environments across the globe delivered through professionals having the skill set to meet societal aspirations There is an increasing demand for trained professionals given the tremendous growth in the physical infrastructure in urban and rural areas particularly the current shortfall in the housing sector besides other tertiary sectors of the economy. The paper addresses the issues about architectural education as mandated through statutory processes prescribed by the Council of Architecture, New India’s Minimum Standards of Architectural Education in India, and the New Education Policy -2020 of the Government of India. The research process adopted identifies the gaps in the current system of education through content and perception analysis through interviews and expert opinions and suggests methodologies that can transform the current processes through an understanding of total quality principles and their application to the higher education sector. The paper evaluates various aspects of architectural education such as institutional mechanisms, the process of admission, curricula structure, pedagogy, teaching-learning processes, scheme of examinations, faculty selection process, and their growth through the perspective of total quality parameters for the service sector. The paper attempts to suggest an approach towards Total Quality in Architectural Education in India driven by quality and research, relevant to the socio-economic, cultural, and physical context of the developing economies in the 21st Century.

Data quality analysis after hyperspectral LiDAR sequentially mapping trees

Abstract. Light detection and ranging (LiDAR), as an innovative remote sensing tool, not only captures target reflectance but also provides its morphological parameters. Traditional single/multi-band LiDAR and multispectral LiDAR (MSL) are presently employed in applications such as 3D modeling and plant biochemical parameter inversion albeit with effectiveness limited. Moreover, hyperspectral LiDAR (HSL) distinguished by its expanded array of spectral detection channels and enhanced spectral resolution, has proven more effective in meeting these requirements and also exhibits superior capabilities in both feature and land cover classification tasks. Nevertheless, point clouds acquired through HSL frequently exhibit quality deficiencies, including uneven density and excessive noise. Meanwhile, there exists a notable absence of technical specifications and operational standards governing the measurement protocols for HSL systems globally. To address this gap, this study constructed a systematic analysis framework of data quality in hyperspectral point clouds and endeavors to qualitatively analyse 30 tree point clouds continuously scanned with Finnish Geospatial Research Institute (FGI) 8-band hyperspectral laser scanner. Furthermore, this research validated the theoretical feasibility of employing the 8-band HSL system for inversion processes aimed at quantifying chlorophyll leaf content. Apart from detecting the time-varying patterns of reflectance within birch canopy point clouds, the results of this study also effectively pinpointed the band exhibiting heightened noise level of the HSL system, demonstrating the efficacy of our proposed quality analysis methodology. The endeavor presented in this study can serve as a cornerstone for advancing hyperspectral LiDAR across a diverse array of related remote sensing and earth observation applications.

The National Cancer Database Conforms to the Standardized Framework for Registry and Data Quality.

Standardization of procedures for data abstraction by cancer registries is fundamental for cancer surveillance, clinical and policy decision-making, hospital benchmarking, and research efforts. The objective of the current study was to evaluate adherence to the four components (completeness, comparability, timeliness, and validity) defined by Bray and Parkin that determine registries' ability to carry out these activities to the hospital-based National Cancer Database (NCDB). Tbis study used data from U.S. Cancer Statistics, the official federal cancer statistics and joint effort between the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI), which includes data from National Program of Cancer Registries (NPCR) and Surveillance, Epidemiology, and End Results (SEER) to evaluate NCDB completeness between 2016 and 2020. The study evaluated comparability of case identification and coding procedures. It used Commission on Cancer (CoC) standards from 2022 to assess timeliness and validity. Completeness was demonstrated with a total of 6,828,507 cases identified within the NCDB, representing 73.7% of all cancer cases nationwide. Comparability was followed using standardized and international guidelines on coding and classification procedures. For timeliness, hospital compliance with timely data submission was 92.7%. Validity criteria for re-abstracting, recording, and reliability procedures across hospitals demonstrated 94.2% compliance. Additionally, data validity was shown by a 99.1% compliance with histologic verification standards, a 93.6% assessment of pathologic synoptic reporting, and a 99.1% internal consistency of staff credentials. The NCDB is characterized by a high level of case completeness and comparability with uniform standards for data collection, and by hospitals with high compliance, timely data submission, and high rates of compliance with validity standards for registry and data quality evaluation.

Meeting the need for effective and standardized neonatology training: a pan-European Master's Curriculum.

Neonatology is a pediatric sub-discipline focused on providing care for newborn infants, including healthy newborns, those born prematurely, and those who present with illnesses or malformations requiring medical care. The European Training Requirements (ETR) in Neonatology provide a framework for standardized quality and recognition of equality of training throughout Europe. The latest ETR version was approved by the Union of European Medical Specialists (UEMS) in April 2021. Here, we present the curriculum of the European School of Neonatology Master of Advanced Studies (ESN MAS), which is based on the ETR in Neonatology and aims to provide a model for effective and standardized training and education in neonatal medicine. We review the history and theory that form the foundation of contemporary medical education and training, provide a literature review on best practices for medical training, pediatric training, and neonatology training specifically, including educational frameworks and evidence-based systems of evaluation. The ESN MAS Curriculum is then evaluated in light of these best practices to define its role in meeting the need for a standardized empirically supported neonatology training curriculum for physicians, and in the future for nurses, to improve the quality of neonatal care for all infants. IMPACT STATEMENT: A review of the neonatology training literature was conducted, which concluded that there is a need for standardized neonatology training across international contexts to keep pace with growth in the field and rapidly advancing technology. This article presents the European School of Neonatology Master of Advanced Studies in Neonatology, which is intended to provide a standardized training curriculum for pediatricians and nurses seeking sub-specialization in neonatology. The curriculum is evaluated in light of best practices in medical education, neonatology training, and adult learning theory.

An imbalance data quality monitoring based on SMOTE-XGBOOST supported by edge computing

Product assembly involves extensive production data that is characterized by high dimensionality, multiple samples, and data imbalance. The article proposes an edge computing-based framework for monitoring product assembly quality in industrial Internet of Things. Edge computing technology relieves the pressure of aggregating enormous amounts of data to cloud center for processing. To address the problem of data imbalance, we compared five sampling methods: Borderline SMOTE, Random Downsampling, Random Upsampling, SMOTE, and ADASYN. Finally, the quality monitoring model SMOTE-XGBoost is proposed, and the hyperparameters of the model are optimized by using the Grid Search method. The proposed framework and quality control methodology were applied to an assembly line of IGBT modules for the traction system, and the validity of the model was experimentally verified.

Interdisciplinary teaching strategy of multimodal data fusion in college aesthetic education curriculum using intelligence methods

In China, aesthetic education at the college level is essential for students’ quality because it improves their understanding of art, helps them progress in their professional career development, and helps them comprehend more fully the attractiveness of creative creations. As a result, it needs to prioritize aesthetic education at the institution and endeavor to nurture students’ feelings progressively and improve their aesthetic abilities at different levels. Artificial intelligence (AI) is used in this project to create a novel, interdisciplinary teaching technique that will maximize students’ artistic and intellectual potential and help them make more, better art. In this research, the Osprey Optimization method improves the interdisciplinary teaching technique for aesthetic education based on a light Exclusive gradient-boosting mechanism (OOM-LEGBM). The exploration-exploitation dynamics of the OOM are incorporated into LEGBM, providing the students with a tangible and relatable technique to understand complex-solving processes. This research develops an enhanced quality framework for college aesthetic education based on the multi-model data fusion system about the implication and necessity of aesthetic education. The influence of college aesthetic education on students’ creative capacity and artistic literacy was investigated to inform instructional activities better to develop students’ aesthetic skills. The experimental findings suggest that the proposed approach achieved an improved accuracy of 99.90%, higher precision of 99.88%, and greater recall of 99.91%. Moreover, it obtained a minimum Root Mean Square Error (RMSE) of 0.26% and a lower Mean Absolute Error (MAE) of 0.34%, showing that the suggested model greatly improved preference learning accuracy while keeping overall accuracy at an identical level. Innovation capacity building in college aesthetic education can help students become more self-aware, improve their study habits, visually literate, and more comprehensive.

Assessing Data Quality in the Age of Digital Social Research: A Systematic Review

While survey data has long been the focus of quantitative social science analyses, observational and content data, although long-established, are gaining renewed attention; especially when this type of data is obtained by and for observing digital content and behavior. Today, digital technologies allow social scientists to track “everyday behavior” and to extract opinions from public discussions on online platforms. These new types of digital traces of human behavior, together with computational methods for analyzing them, have opened new avenues for analyzing, understanding, and addressing social science research questions. However, even the most innovative and extensive amounts of data are hollow if they are not of high quality. But what does data quality mean for modern social science data? To investigate this rather abstract question the present study focuses on four objectives. First, we provide researchers with a decision tree to identify appropriate data quality frameworks for a given use case. Second, we determine which data types and quality dimensions are already addressed in the existing frameworks. Third, we identify gaps with respect to different data types and data quality dimensions within the existing frameworks which need to be filled. And fourth, we provide a detailed literature overview for the intrinsic and extrinsic perspectives on data quality. By conducting a systematic literature review based on text mining methods, we identified and reviewed 58 data quality frameworks. In our decision tree, the three categories, namely, data type, the perspective it takes, and its level of granularity, help researchers to find appropriate data quality frameworks. We, furthermore, discovered gaps in the available frameworks with respect to visual and especially linked data and point out in our review that even famous frameworks might miss important aspects. The article ends with a critical discussion of the current state of the literature and potential future research avenues.

Risk adjusted EWMA control chart based on support vector machine with application to cardiac surgery data

In the current study, we demonstrate the use of a quality framework to review the process for improving the quality and safety of the patient in the health care department. The researchers paid attention to assessing the performance of the health care service, where the data is usually heterogeneous to patient’s health conditions. In our study, the support vector machine (SVM) regression model is used to handle the challenge of adjusting the risk factors attached to the patients. Further, the design of exponentially weighted moving average (EWMA) control charts is proposed based on the residuals obtained through SVM regression model. Analyzing real cardiac surgery patient data, we employed the SVM method to gauge patient condition. The resulting SVM-EWMA chart, fashioned via SVM modeling, revealed superior shift detection capabilities and demonstrated enhanced efficacy compared to the risk-adjusted EWMA control chart.

The impact of regulation on the quality of care in nursing homes in Ireland: a time-series analysis of change in compliance

Background Quality in health and social care is of paramount importance. Regulation is often used for ensuring or promoting quality in care services. Services are typically regulated by independent public authorities, which monitor services for compliance with regulations. There is limited research, however, on services’ compliance with regulations to provide a high quality of care. This study aims to examine nursing home compliance with regulations relating to quality. Methods Secondary legislation used for regulating nursing homes in Ireland was mapped to the Donabedian’s quality framework with each regulation categorised as either “structure”, “process” or “outcome”. The regulations categorised as “outcome” were determined to be quality-related regulations; such regulations were extracted and became this study’s area of focus. Published inspection reports from the regulator in Ireland for a three year period (2019 to 2021) (n=1,153) were assessed. The frequency with which the “outcome regulations” were inspected in nursing homes, and the proportion of compliance achieved, was calculated. Change in compliance levels across the three years was evaluated using Chi2 tests. Results Ten regulations were categorised as “outcome regulations” and addressed the following areas: positive behaviour; protection; residents' rights; communication; visits; personal possessions; food and nutrition; information for residents; medicines and pharmaceutical services; and complaints procedures. Compliance with two regulations (‘Regulation 9: Residents’ rights’ and ‘Regulation 11: Visits’) significantly improved during the three years (p<0.05). Compliance with ‘Regulation 12: Personal Possessions’ significantly decreased over the three years, however, so did the proportion found not compliant. While there was no significant change in the other regulations examined, compliance trended towards improvement, except for one regulation, ‘Regulation 20: Information for residents’, which trended downwards. Conclusion This analysis of national-level data found improvements in compliance across nearly all quality-related regulations demonstrating that regulation can be a positive influence on quality maintenance and improvement.

Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.

This work describes the process by which the quality of electronic health care data for a public health study was determined. The objectives were to adapt, develop, and implement data quality assessments (DQAs) based on the National Institutes of Health Pragmatic Trials Collaboratory (NIHPTC) data quality framework within the three domains of completeness, accuracy, and consistency, for an investigation into oral health care disparities of a preventive care program. Electronic health record data for eligible children in a dental accountable care organization of 30 offices, in Oregon, were extracted iteratively from January 1, 2014, through March 31, 2022. Baseline eligibility criteria included: children ages 0-18 with a baseline examination, Oregon home address, and either Medicaid or commercial dental benefits at least once between 2014 and 2108. Using the NIHPTC framework as a guide, DQAs were conducted throughout data element identification, extraction, staging, profiling, review, and documentation. The data set included 91,487 subjects, 11 data tables comprising 75 data variables (columns), with a total of 6,861,525 data elements. Data completeness was 97.2%, the accuracy of EHR data elements in extracts was 100%, and consistency between offices was strong; 29 of 30 offices within 2 standard deviations of the mean (s = 94%). The NIHPTC framework proved to be a useful approach, to identify, document, and characterize the dataset. The concepts of completeness, accuracy, and consistency were adapted by the multidisciplinary research team and the overall quality of the data are demonstrated to be of high quality.

Quality of education in migration hotspot areas in Ethiopia: Input‐process‐outcome‐context approach

AbstractWhile the lack of educational opportunities limits the future of most people in Ethiopia, they pose dire consequences to young people in migration hotspot areas. Using input‐process‐outcome‐context‐education quality framework, this study investigated the quality of education in eight migration hotspot areas of Ethiopia and its association with migration. A random sample of 1,187 participants (793 students, 262 teachers and 132 parents) completed a survey. Data were analysed using descriptive and inferential statistics. Results indicated that participants’ overall rating of education quality was low. Over a third of participants perceived that input, process and outcome components of education in migration hotspot areas have a quality problem. Over 80% of the participants believed that poor quality of education could be a cause for migration. More specifically, a significant portion of the participants (37%) reported that there was no pedagogical centre in their respective schools; continuous assessment was practiced rarely (36%); and creativity and discovery among students was almost non‐existent (40%). The participants' ratings indicated that the top five factors that contribute to the poor quality of education are low level of teacher competence, poor student motivation, poverty, value attached to education and large class size.

Master Data Quality Management Framework: Content Validity

Organizations rely on high quality master data as a critical component in achieving their operational and strategic performance. To accomplish high quality master data, they need to be managed properly through a systematic and holistic framework. However, prevalent master data quality management frameworks lack in providing comprehensive management practice in assuring the quality of master data. Hence, stimulates the need to develop an improved master data quality framework. Prior to the development of the framework, the identification and validation of factors that contribute to the management of master data quality must be performed. Thus, this paper underlined four elements and seven factors affecting master data quality management. Further, the identified factors were validated using a questionnaire as the validation instrument. The questionnaire consists of 95 items representing the identified seven factors that were derived from previous studies in the domain of total quality management, data quality management, and master data. Since the items are derived from the different contexts of study, content validation is a need. Previous research has suggested several techniques for performing content validation, covering both quantitative and qualitative approaches. The quantitative approach employed objective assessment and the result was statistically analysed. While the qualitative approach adopted subjective assessment such as comments, ideas, or respond. In this paper, the quantitative approach is selected over the qualitative approach, considering the effort in analyzing several items (95 items) is less complex compared to the qualitative approach which is more difficult to interpret and account for biased results. The selected panel of experts validate the instrument using a three-point scale namely “1 = not relevant”, “2 = important (but not essential)”, and “3 = essential”. Later, using the technique proposed by Lawshe, the value of the content validity ratio (CVR) is calculated. As a result, 92 items are accepted, and 3 items are rejected. The elimination of the 3 items is due to the unsuitableness to be used in the context of the public sector. The validated items can be used as an instrument to validate the factors affecting master data quality management. The proposed factor would support the organization in managing master data quality more effectively.  

The key themes of issues and challenges in developing the quality framework for football coaching education in Malaysia

Sports coaching education and development has consistently been a topic of discussion on a global scale. Coaching education is considered to be essential for both sustaining and improving the quality of sports coaching. The development of a quality framework for sports education will serve as a guide to ensure consistency, relevance, and effectiveness in coaching and enable coaches to optimise athlete development and performance. This study aims to discuss the issues and challenges of developing the Malaysia Football Coaching Education Quality Framework. This study uses the qualitative method of Evolved Grounded Theory (EGT). The data from five football coaches (n=5) nationwide from the National Sports School, State Sports School, District Training Centre and Tunas Academy representing the executing group and five football stakeholders (n=5) representing the management group were collected from semii-structured in-depth interviews, observations and writing documents to determine the main issues and challenges. The data were transcribed verbatim and analysed using Atlas.ti software through open coding, axial coding and selective coding processes. The following three themes emerged in the context of issues and challenges in developing the Malaysia Football Coaching Education Quality Framework: 1) Accredited Coaching Course/Coaching Certification, 2) Opportunity to Attend Coaching Course, and 3) Coaching Syllabus. Therefore, the findings will help national football stakeholders develop the Malaysia Football Coaching Education Quality Framework. Through this framework, stakeholders and governing bodies can cooperate to upgrade the existing program and improve the quality of football coaching education in Malaysia. In fact, quality football coaches can help to produce quality footballers in Malaysia. In addition, the Malaysia Football Coaching Education Quality Framework can become a guideline for football coaches in Malaysia

How do language education researchers attend to quality in qualitative studies?

Abstract The steady expansion in qualitative research in the area of language education over the last two decades indicates the growing recognition of its importance to investigating issues of language teaching and learning. Along with this recognition, understanding and assessing the quality of qualitative studies in this area has gained increasing significance. Addressing this concern, in this research synthesis, we qualitatively explore how 236 qualitative language education studies published in seven leading journals explicitly foreground the issue of ‘research quality’. We conducted a qualitative content analysis of how authors of these studies addressed the main quality concepts proposed by well-known frameworks of qualitative research quality. Our findings, presented as ten major themes, show that qualitative researchers' overt treatment of research quality is realised based on three distinct orientations: no explicit quality criteria, positivist views of quality, and interpretive quality conceptions. We discuss aspects of these orientations and their implications for qualitative research in language education.

Unravelling the dynamics of mental health inequalities in England: A 12-year nationwide longitudinal spatial analysis of recorded depression prevalence

BackgroundDepression is one of the most significant public health issues, but evidence of geographic patterns and trends of depression is limited. We aimed to examine the spatio-temporal patterns and trends of depression prevalence among adu lts in a nationwide longitudinal spatial study in England and evaluate the influence of neighbourhood socioeconomic deprivation in explaining patterns. MethodsInformation on recorded depression prevalence was obtained from the indicator Quality and Outcomes Framework: Depression prevalence that measured the annual percentage of adults diagnosed with depression for Lower Super Output Areas (LSOA) from 2011 to 2022. We applied Cluster and Outlier Analysis using the Local Moran’s I algorithm. Local effects of deprivation on depression in 2020 examined with Geographically Weighted Regression (GWR). Inequalities in recorded prevalence were presented using Prevalence Rate Ratios (PRR). ResultsThe North West Region of England had the highest concentration of High-High clusters of depression, with 17.4% of the area having high values surrounded by high values in both space and time and the greatest percentage of areas with a high rate of increase (43.1%). Inequalities widened among areas with a high rate of increase in prevalence compared to those with a lower rate of increase, with the PRR increasing from 1.66 (99% CI 1.61–1.70) in 2011 to 1.81 (99% CI 1.76–1.85) by 2022. Deprivation explained 3%–39% of the variance in depression in 2020 across the country. ConclusionsIt is crucial to monitor depression's spatial patterns and trends and investigate mechanisms of mental health inequalities. Our findings can help identify priority areas and target prevention and intervention strategies in England. Evaluating mental health interventions in different geographic contexts can provide valuable insights to policymakers on the most effective and context-sensitive strategies, enabling them to allocate resources towards preventing the worsening of mental health inequalities.

Quality measures of virtual care in ambulatory healthcare environments: a scoping review

ObjectivesDelivery of virtual care increased throughout the COVID-19 pandemic and persisted after physical distancing measures ended. However, little is known about how to measure the quality of virtual care, as...

A Data-Driven Autonomous Assessment Framework for Education Quality Based on Multiscale Deep Learning

Nowadays, computational intelligence-assisted autonomous assessment of education quality has become a more and more general concern in the area of smart education management. As education quality assessment is a complicated process with multiple heterogeneous factors, it remains challenging to make effective assessment using simple information modality and criteria. To deal with this issue, this paper introduces multiscale deep learning, and proposes a novel data-driven autonomous assessment framework for education quality. In particular, four aspects of heterogeneous indicators are selected as the basic indexes and a dilated convolutional neural network structure is formulated to perform multiscale feature extraction. Then, the structural equation is adopted to make multivariate characterization and output the final assessment results. At last, some simulations are carried out on realistic education operation data to evaluate the efficiency of the proposed autonomous assessment framework. Two aspects of findings can be deduced from the results. For one thing, multisource fusion of multiple indicators well makes sense to autonomous education assessment. For another, multiscale deep learning can provide some beneficial promotion to assessment efficiency.

Student participation in Irish primary schools

ABSTRACT This research explores the perceptions of 125 primary school students (ages 8–12) in two Educate Together schools in Ireland. As a national network, established over 40 years, Educate Together schools are characterised as equality-based, child-centred, participative and democratic. This ethos is set out in the Educate Together Charter and clarified through the recent development of the Educate Together Ethos Quality Framework. This article explores the understanding of student participation in literature and what it means in practice for Educate Together schools. Using a survey to gather data, the key research questions are as follows: How do students perceive their participation in school: (1) in the classroom; (2) outside the classroom and (3) in decision-making at the whole school level. This research attempts to build on the small number of previous studies where student participation in school was explored from the student's perspective. The descriptive quantitative and qualitative findings indicate a relatively high level of participation across these key arenas of school life, and the findings differentiate between those participation activities that are experienced, more or less commonly among students. The study considers the enabling environment created by Educate Together to ensure that student participation is part of school culture.

Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion and Image Attribute Editing

GAN-based image attribute editing firstly leverages GAN Inversion to project real images into the latent space of GAN and then manipulates corresponding latent codes. Recent inversion methods mainly utilize additional high-bit features to improve image details preservation, as low-bit codes cannot faithfully reconstruct source images, leading to the loss of details. However, during editing, existing works fail to accurately complement the lost details and suffer from poor editability. The main reason is they inject all the lost details indiscriminately at one time, which inherently induces the position and quantity of details to overfit source images, resulting in inconsistent content and artifacts in edited images. This work argues that details should be gradually injected into both the reconstruction and editing process in a multi-stage coarse-to-fine manner for better detail preservation and high editability. Therefore, a novel dual-stream framework is proposed to accurately complement details at each stage. The Reconstruction Stream is employed to embed coarse-to-fine lost details into residual features and then adaptively add them to the GAN generator. In the Editing Stream, residual features are accurately aligned by our Selective Attention mechanism and then injected into the editing process in a multi-stage manner. Extensive experiments have shown the superiority of our framework in both reconstruction accuracy and editing quality compared with existing methods.