Description Of Items Research Articles

Translation and Validation of the Diabetes Knowledge Questionnaire in Indonesian Patients With Type 2 Diabetes.

The purpose of the study was to translate and cross-culturally adapt the 24-item Diabetes Knowledge Questionnaire (DKQ) for Indonesian patients with type 2 diabetes (T2D) and evaluate its psychometric properties. Forward-backward translation, adaptation involving 7 experts, and pretesting to develop the Indonesian version of DKQ were conducted. Psychometric analysis was carried out among T2D patients from 40 primary health care centers in Indonesia. Known-group, convergent and discriminant validity, internal consistency and test-retest reliability were assessed. Additionally, a descriptive item analysis was conducted. In total, 39 patients participated in the pretesting and 304 patients in the validation process and descriptive analysis. Of the 24 items, 2 were adjusted during the adaptation process, and 1 item was deleted because it did not adequately reflect the original item. Known-group validity was demonstrated because patients with younger ages, higher educational levels, and longer diabetes duration had significantly higher DKQ scores. Convergent validity was demonstrated by a significant positive correlation of the DKQ scores with overall treatment satisfaction. The 23-item DKQ Bahasa Indonesia showed satisfactory internal consistency (Cronbach's α = 0.73; omega total = 0.72) and good test-retest reliability (intraclass correlation coefficient = 0.87 in a sample of 27 patients). No floor and ceiling effects were found in the item analysis. The study demonstrates adequate validity and reliability of the 23-item DKQ Bahasa Indonesia for assessing diabetes knowledge in Indonesian primary care patients with T2D. This instrument can be used to identify room for improvement and develop diabetes education programs.

Solo music performance assessment criteria: a systematic review.

Assessment is a crucial aspect of music performance. In pedagogical contexts, an effective assessment process can measure student achievement and inform instructional decisions that contribute to improving teaching and learning. However, music performance assessment is particularly challenging due to its inherent subjectivity, involving personal expression and interpretation, which can lead to divergent opinions. In this PRISMA systematic review (registration DOI: 10.17605/OSF.IO/CSM8Q), we aimed to delimit and analyze solo music performance assessment systems found in the literature to date, including their corresponding evaluation categories and descriptive criteria, rating methodology, and target audience. A search in three main scientific databases (Web of Science, Scopus, ERIC) was conducted using keywords associated with the topic of assessment in the field of solo music performance. Ultimately, 20 papers were selected and examined, resulting in 26 original assessment systems for analysis. Regarding sample characteristics, we found that studies mainly focused on evaluating high school and university students, with music teachers and faculty members serving as primary evaluators. Many assessment systems were designed to be applicable across various instruments, although some were tailored to specific instruments (e.g., piano, voice) and families (e.g., brass, woodwind). Systems typically structured evaluation around technical, interpretative/expressive, and various musical feature categories (e.g., pitch, rhythm, intonation), further elaborated with descriptive items. While five-point Likert scales were commonly used, recent studies indicated a shift towards rubrics for detailed feedback, which aids examiners' understanding and supports student progress. No differentiation was found in assessment criteria based on students' learning stages, suggesting an area for improvement in refining these assessment methods. This study identifies gaps and proposes improvements in existing assessment systems, providing a foundation for educators and policymakers to enhance curriculum design and instructional practices in music education.

Tracing the Contours of Archival Silences: A Case Study of Critical Collection Building on the Rock Springs Massacre

ABSTRACTArchives can reinforce silences of marginalized communities, further erasing their stories and voices. This study aims to understand the contours of archival silences, focusing on experiences of Chinese people in the United States in the late 19th and early 20th centuries. We are building a digital archival collection that centers Chinese peoples' voices around the Rock Springs Massacre of 1885, an incident of racial violence that killed at least 28 Chinese people and displaced hundreds more. As we selected items, we wrote structured reflections to critically examine each item's content, source, presentation, arrangement and description in their original context, as well as any absences and silences that we encountered in the search and assessment process. Based on a praxis of radical empathy, ethics of care, and slow archives, we engaged with our positionalities, past experiences, professional training in Library and Information Sciences (LIS), and affective responses throughout the research process to reflect on how they shape our decisions and hesitations in item selection and contextualization. Three main themes emerged from a preliminary thematic analysis of our reflections: complexity of archival silences and silencing, challenges of inclusive representation, and problematizing and going beyond primary sources.

Telehealth Education in Athletic Training

Context: Within the broad telehealth education literature, there are numerous case studies and position statements from various associations of model practices. Little is known about how athletic training is addressing telehealth education. Objective: To determine how athletic training education programs teach, assess, and structure telehealth content. Design: Cross-sectional study Setting: Online survey instrument Patients or Other Participants: An institutional athletic training faculty member from 82 athletic training programs in the United States. Data Collection and Analysis: The survey contained the validated Telehealth Evaluative Content in Higher Education (TECHE) instrument, which consisted of descriptive items focused on how education programs teach, assess, and deliver telehealth education content. Data were cleaned and basic descriptive statistics were calculated. Results: Of the program faculty that responded, 49% (n=39) reported that their program includes telehealth content. Specifically, 19 programs (49%) taught about the knowledge, skills, and abilities using multiple presentations across the curriculum (42%, n = 8) ,including techniques such as class discussions (68%, n = 13) and lectures (63%, n = 12). For assessment, 17 programs (44%) included simulations (65%, n = 11) evaluated most commonly by faculty (82%, n = 14). Only 14 (36%) programs created telehealth clinical experiences including practice with simulated patients throughout all the domains of telehealth skills. When programs included telehealth clinical experiences, they mainly focused (28%, n = 16) on orthopedics and (26%, n = 15) on primary care experiences. Discussion: The development of telehealth content should be based on model practices and delivered across multiple curriculum presentations. Assessment should incorporate various perspectives and utilize different methods. Practice opportunities with classmates and simulated patients that require interpersonal communication, data gathering, and patient education should be developed. Conclusion: While a slight majority of programs do not teach telehealth content, those that do teach telehealth utilize various strategies. Most program faculty use simulations conducted by faculty to assess orthopedic and primary care telehealth content.

Toward Attribute-Controlled Fashion Image Captioning

Fashion image captioning is a critical task in the fashion industry that aims to automatically generate product descriptions for fashion items. However, existing fashion image captioning models predict a fixed caption for a particular fashion item once deployed, which does not cater to unique preferences. We explore a controllable way of fashion image captioning that allows the users to specify a few semantic attributes to guide the caption generation. Our approach utilizes semantic attributes as a control signal, giving users the ability to specify particular fashion attributes (e.g., stitch, knit, sleeve) and styles (e.g., cool, classic, fresh) that they want the model to incorporate when generating captions. By providing this level of customization, our approach creates more personalized and targeted captions that suit individual preferences. To evaluate the effectiveness of our proposed approach, we clean, filter, and assemble a new fashion image caption dataset called FACAD170K from the current FACAD dataset. This dataset facilitates learning and enables us to investigate the effectiveness of our approach. Our results demonstrate that our proposed approach outperforms existing fashion image captioning models as well as conventional captioning methods. Besides, we further validate the effectiveness of the proposed method on the MSCOCO and Flickr30K captioning datasets and achieve competitive performance.

From pre-owned printers to pristine Porsches

Abstract Recent years have seen a considerable increase in e-commerce, with sales forecast to continue rising over the coming years. This study provides a corpus linguistic analysis of item descriptions on eBay’s UK website, on which both members of the public and businesses can offer goods for sale. It is based on two corpora of items sold on the site in 2015 and 2020 which together contain 412,601 item descriptions and over 57 million words of text. The analysis applies corpus linguistic methods to gain further insight into the diachronic development of language use on eBay, to explore linguistic features in item descriptions and across product categories, and to relate word choice to product selling price. Its findings offer new understanding of the changing language of online selling and indicate how a corpus linguistic methodology may be used to explore the impact of linguistic features on sales figures.

Development and validation of the metaverse perception scale for nursing students

BackgroundThe use of Metaverse applications in nursing education is increasing every day and the evaluation of nursing students' perceptions of this new digital technology may guide interventions related to the use of the Metaverse. However, currently, no standardized instrument specifically designed to measure nursing students' perceptions of the Metaverse exists in Turkey. AimTo develop and validate the Metaverse Perception Scale for Turkish nursing students. MethodThis methodological study was carried out between March 1, 2023 and June 30, 2023 and its sample consisted of 770 nursing students who were enrolled in undergraduate nursing departments of universities in Turkey. The item pool was created based on a literature review and data were analyzed using descriptive statistics, item analysis, exploratory and confirmatory factor analysis, convergent and divergent validity, correlation analysis, internal consistency reliability and test-retest analysis. ResultsThe content validity index for the items ranged between 0.85 – 1.00. According to exploratory factor analysis, the eigenvalue of four factors was greater than one. These four factors explained 77.28 % of the total variance. In the scale model developed for this study, model fit was found to be acceptable for goodness of fit index:.921 and root mean square error of approximation:.064. χ2/sd: 1.659, normalized fit index:.961 and chi-square/degree of freedom revealed that model fit was at a good level and within the recommended value ranges. It was determined that the scale showed convergent and divergent validity and Cronbach's Alpha value ranged between 0.95 and 0.93 in the subscales. The Metaverse Perception Scale in Nursing Students was found to have a high level of test-retest reliability. ConclusionThis study showed that the scale, which consists of 24 items and four subscales, is a valid and reliable measurement tool that can be used to assess nursing students' perceptions of the Metaverse in the Turkish context. This scale can contribute to the evaluation of Turkish nursing students' perceptions of the Metaverse, as well as the effective management of processes related to Metaverse practices.

Development of Omani-branded food composition database for an electronic dietary assessment tool

Background: The food composition database provides a comprehensive information on the various nutrients present in the foods. At present, Oman lacks a food composition database (FCDB) of locally produced branded foods, which necessitates the development of such a database. Objectives: The aim of this study is to develop an FCDB for Omani-branded foods available for local consumption for an electronic dietary assessment tool. Methods: Back-of-pack (BOP) nutritional information of these branded foods available in our markets was gathered from manufacturer data. Food mapping was used to match the branded foods according to the BOP macronutrient data and food description to the appropriate generic food item from United States Department of Agriculture (USDA) or Canadian FCDBs. Results: The developed database is composed of 571 food items with associated 60 nutrients. The majority of food items (91%) were mapped to a single generic food item. The rest (9%) was mapped to multiple generic food items. Overall, 96% of food items were mapped to a single generic food, which was matched based on BOP macronutrients and item descriptions. However, the minority (4%) were mapped based on item description alone as either the BOP nutrients were implausible. Moreover, 91% of food items were individually mapped to within 10% agreement with the generic food item for energy. The fish and fish group has the largest mean of absolute percentage difference in energy between BOP and generic items (16%). Conclusion: This currently developed database would critically help to accurately assess the dietary intake of the Omani population once incorporated into an electronic dietary assessment tool, and it can be updated in the future on a regular basis.

Archaeological Collections from the Jōmon Period in the Peter the Great Museum of Anthropology and Ethnography (Kunstkamera)

This article presents an analysis and additional description of archaeological items of the Jōmon period from A.V. Grigoriev’s collection (No. 1294) at the Department of Archaeology of the Peter the Great Museum of Anthropology and Ethnography (Kunstkamera) RAS in St. Petersburg. The study focuses on the description of decorative motifs and the stylistic attribution of selected samples of pottery. The analysis is based on the published data about the Ōmori shell mound (Tokyo, Honshu Island), visited by Grigoriev in 1878 as a part of the expedition from the Imperial Russian Geographical Society. The early stage of the Japanese archaeology is described with reference to the Ōmori shell mound. Special attention is given to specific features of the Jōmon decorative style. The geographic location of the site suggests that the samples are associated with the Kasori B and Horinouchi styles. Contrary to the Russian tradition, the emphasis is made on stylistic interpretation rather than technology and typology. The combinations of large zonally arranged rectangular designs and spiral motifs are typical of the Kasori B style, to which several samples belong. Others reveal vertically and horizontally arranged patterns consisting of incised arcuate and straight lines, typical of the Horinouchi style.

Comparison of Scoring Results for Physics Descriptive Items in GPT Language Models Based on Prompts

Comparison of Scoring Results for Physics Descriptive Items in GPT Language Models Based on Prompts

DIRECT: Dual Interpretable Recommendation with Multi-aspect Word Attribution

Recommending products to users with intuitive explanations helps improve the system in transparency, persuasiveness, and satisfaction. Existing interpretation techniques include post-hoc methods and interpretable modeling. The former category could quantitatively analyze input contribution to model prediction but has limited interpretation faithfulness, while the latter could explain model internal mechanisms but may not directly attribute model predictions to input features. In this study, we propose a novel D ual I nterpretable Rec ommenda t ion model called DIRECT, which integrates ideas of the two interpretation categories to inherit their advantages and avoid limitations. Specifically, DIRECT makes use of item descriptions as explainable evidence for recommendation. First, similar to the post-hoc interpretation, DIRECT could attribute the prediction of a user preference score to textual words of the item descriptions. The attribution of each word is related to its sentiment polarity and word importance, where a word is important if it corresponds to an item aspect that the user is interested in. Second, to improve the interpretability of embedding space, we propose to extract high-level concepts from embeddings, where each concept corresponds to an item aspect. To learn discriminative concepts, we employ a concept-bottleneck layer, and maximize the coding rate reduction on word-aspect embeddings by leveraging a word-word affinity graph extracted from a pre-trained language model. In this way, DIRECT simultaneously achieves faithful attribution and usable interpretation of embedding space. We also show that DIRECT achieves linear inference time complexity regarding the length of item reviews. We conduct experiments including ablation studies on five real-world datasets. Quantitative analysis, visualizations, and case studies verify the interpretability of DIRECT. Our code is available at: https://github.com/JacksonWuxs/DIRECT .

Determining the psychometric properties of a written test to assess safe dental practice

IntroductionSafe practice in medicine and dentistry has been a global priority area in which large knowledge gaps are present.Patient safety strategies aim at preventing unintended damage to patients that can...

Automotive Kit Demand Forecasting Using Advanced Forecasting Models: A Data-Driven Approach for Optimal Demand Forecasting

This study addresses the major challenges of forecasting automotive kit items(parts of vehicles) by enhancing the delivery of the products and managing the inventory. The kit items vary as per customers and it is unique on its own, where the uniqueness determines the vehicle parts. Customers are the major role players who provide the business hence, this study highlights various factors contributing to the customer’s choice of kit items with features consisting of vehicle name, original equipment manufacturer (OEM), Item Description (collection of vehicle parts) type of product (brand of vehicle) and monthly allotment of each kit item as per customer starting from 2021 April to 2024 January. We conducted an extensive analysis to assess a range of time series analysis techniques for predicting kit demand within the automotive industry, the methods we investigated encompassed Autoregressive (AR), Autoregressive Moving Average (ARMA) ,Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Simple Exponential Smoothing (SES), Holt's Linear Trend Method - Double Exponential Smoothing, Triple Exponential Smoothing - Holt Winters, Long Short-Term Memory (LSTM) and advanced forecasting models such as prophet in evaluating the accuracy of these models, we employed key metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), this study aims to drive significant progress in the automotive industry by optimising inventory management reducing storage costs and improving delivery efficiency to ensure smooth business operations moreover the integration of visually engaging dashboards for real-time analysis of projected values plays a pivotal role in identifying crucial monthly demand trends this integration not only enhances operational efficiency but also fosters enriched customer engagement thereby facilitating sustained advancement within the automotive sector.

Цветные и благородные металлы в погребении № 115 кокпомъягского могильника вымской культуры: археологический и археолого-минералогические аспекты

The paper discusses the results of archeological and archeomineralogical analyses of items made of non-ferrous and precious metals from burial No. 115 of the Kokpomyag burial ground of the Vym culture of the Vychegda Perm, dating back to the XI–XIV centuries. It includes a general characteristics of the burial ground, burial No. 115, a description of bronze and silver items, and the results of experimental studies of these items using a complex of modern mineralogical and geochemical methods. The results obtained indicate that local bronze casters possessed sophisticated technologies for the production of a variety of tin-copper and tin-silver alloys.

Structural Validity Evidence for the Oxford Utilitarianism Scale Across 15 Languages

Abstract: Background: The Psychological Science Accelerator (PSA) recently completed a large-scale moral psychology study using translated versions of the Oxford Utilitarianism Scale (OUS). However, the translated versions have no validity evidence. Objective: The study investigated the structural validity evidence of the OUS across 15 translated versions and produced version-specific validity reports. Methods: We analyzed OUS data from the PSA, which was collected internationally on a centralized online questionnaire . We also collected qualitative feedback from experts for each translated version. Results: For each version, we produced version-specific psychometric reports which include the following: (1) descriptive item and demographics analyses, (2) factor structure evidence using confirmatory factor analyses, (3) measurement invariance testing across languages using multiple-group confirmatory factor analyses and alignment optimization, and (4) reliability analyses using coefficients α and ω.

Multifamily group evaluation with Score 15 questionnaire

IntroductionThe multifamily group that has been underway since April 2019 in Alcobendas, Madrid is described. A group that serves people diagnosed with mental disorder and their families, with the aim of improving their health and quality of life. It is about facilitating and improving the basic communication of relational aspects and healthy bonds. It is intended to offer a space where you can think together about the experiences lived in your own family with the rest of the groupObjectivesAssess the evolution and improvement of the patient and family members with the Score 15 questionnaire, The Score is a way of giving users a voice about the therapy process, not about the contents of their problems, but about their perception of the effectiveness of therapeutic work and for professionals it is an opportunity to obtain important feedback from their work.MethodsUsing the Score !5 questionnaire on all participants in the group at time zero and after 12 sessionsResultsImprovement in the family description items, and in the quantitative improvement in scoring of the following questions: What degree of severity would indicate? Do you think therapy will be helpful/has it been helpful to you?ConclusionsFamily therapy in the modality of Multifamily Groups provides an improvement in intrafamily communication, its links and therefore in the rest of social communication, facilitating the exit from loneliness and misunderstanding and in turn broadens the understanding and understanding by therapistsDisclosure of InterestNone Declared

Emotional intelligence in young emerging adults: A focus on Wong and Law's scale in the digital work sphere

Despite the growing research on the usefulness and validity of the four-factor Wong and Law Emotional Intelligence Scale (WLEIS), empirical evidence for its relevance to Black African young emerging adults seems non-existent. The study's objective was to assess the relevance of the original WLEIS factor structure for a sample (N = 365) of South African Technical and Vocational Education and Training (TVET) final year students (mean age = 24.3 years; SD = 2.38). Exploratory and confirmatory factor analyses, and convergent and discriminant validity tests revealed a three-factor first-order structure with uniquely descriptive items characteristic of the sample's emotional intelligence. The findings contribute to the WLEIS measurement of emotional intelligence in young emerging adults who are preparing to enter the digital-age work world.

Cross Category Recommendations Using LLMs

Cross-category recommendation systems aim to suggest items to users from various categories, enhancing the user experience by broadening their exposure to diverse products or services. Large Language Models (LLMs) offer a promising avenue for improving the effectiveness of such systems. LLMs, trained on vast datasets, excel at understanding complex relationships between different categories of products by analysing patterns in user interactions, preferences, and item descriptions. By leveraging natural language understanding, these models can identify similarities and connections between disparate product categories, enabling more personalized and relevant cross-category recommendations. This approach goes beyond traditional recommendation algorithms that typically focus on a single category or rely heavily on collaborative filtering techniques. Instead, LLMs can process and integrate information from multiple domains, making it easier to suggest complementary products from different categories that users may not have initially considered. Additionally, LLMs can interpret contextual information, such as user reviews and sentiment analysis, to further refine recommendations based on the user's unique preferences and behavioural patterns. The potential of LLMs in cross-category recommendations opens new possibilities for e-commerce, entertainment, and other industries where personalized experiences are key to user engagement and satisfaction. As these models continue to evolve, they offer the promise of more intelligent and comprehensive recommendation systems that can better meet the needs of users by offering diverse and relevant suggestions across categories. These abstract outlines the key role of LLMs in advancing cross-category recommendation systems, highlighting their capacity to improve personalization and broaden the scope of recommendations.

Validity and reliability study of Turkish version of abbreviated science anxiety scale

The aim of this study is to adapt “Abbreviated Science Anxiety Scale”(ASAS) developed by Megreya et al. (2021) to Turkish and to investigate the reliability and validity of Turkish-ASAS. In the adaptation study, some steps were determined and implemented. After the English-Turkish translation validity of ASAS was ensured, data were collected within the scope of the survey study for reliability and validity studies. The study group in the scale adaptation study are experts (teaching staff in science education, Science teacher, and English teacher) and 410 middle school students (Grade 5, 6, 7 and 8). After the descriptive statistics and item analysis were made, a exploratory and confirmatory factor analysis was performed to reveal the factor structure. The adaptation stage findings shows the two-factor structure of ASAS. These factors are “learning science anxiety” and “science evaluation anxiety”. Cronbach Alpha reliability coefficient for ASAS-Turkish was calculated as 0.703. As in the original scale, ASAS-Turkish is a 5-point Likert type scale and consisting of 9 items. The results show that ASAS-Turkish is a short, valid and reliable tool. Low scores on the ASAS indicate low anxiety and high scores indicate high anxiety. It can be used to guide and enhance scientific study.

A Validation Study of the Extended Relevance Scale using the D3mirt Package for R

ABSTRACT This paper reports a validation study based on descriptive multidimensional item response theory (DMIRT), implemented in the R package D3mirt by using the ERS-C, an extended version of the Relevance subscale from the Moral Foundations Questionnaire including two new items for collectivism (17 items in total). Two latent models are created and investigated in two large samples (N = 1560 and N = 1380 US citizens). The internal investigation indicates a presence of two unidimensions, Compassion and Conformity, and a third within-multidimension of Collectivism in both samples. The external investigation show that Republicans are the highest on Conformity in the models, followed by Independents, and lastly Democrats. Democratic followers are the highest on compassion, followed by Independents and Republicans. No difference between groups on Collectivism were found. Lastly, the models succeeded in correctly identifying the Republican and Democratic voters but struggled to correctly classify the Independent.