Determinants of the Utilization of Recognized Traditional and Complementary Medicine Service in Malaysia: A Secondary Analysis of a Cross-Sectional Population-Based Survey.

INTRODUCTION: The utilization of traditional and complementary medicine (T&CM) services has witnessed a global increase over the past decades. Currently, seven practice areas are recognized in Malaysia: traditional Malay medicine (TMM), traditional Chinese medicine (TCM), traditional Indian medicine, homeopathy, Islamic medical practice, chiropractic, and osteopathy. Many global studies have investigated the general determinants of T&CM service utilization. However, there has been no comprehensive study reporting specific determinants of recognized T&CM service utilization. This study aimed to examine the prevalence and specific determinants of recognized T&CM service utilization in Malaysia at a national level. METHODS: This study is a secondary analysis of data from the National Health and Morbidity Survey (NHMS), a cross-sectional population-based survey conducted in Malaysia in 2015. A total of 6,207 respondents aged ≥18 years were included in this study. The associations of sociodemographic, health, and lifestyle factors with the utilization of the three T&CM service categories (TMM, TCM, and other T&CM) were examined using three separate logistic regression analyses. RESULTS: The prevalence of recognized T&CM service utilization in the last 12 months was 19.3%, with 15.0% for TMM, 3.9% for TCM, and 1.5% for other T&CM. The determinants of TMM service utilization were female sex; age range of 18-29 years; married or widowed/divorced status; Malay, indigenous, or other ethnicities; high household income; and being overweight/obese. The determinants of TCM service utilization were the age range of 30-49 years, urban residential location, Chinese ethnicity, and adequate fruit and vegetable intake. No specific determinants were identified for other T&CM. CONCLUSION: This study provided novel evidence of a strong ethnocultural ownership toward traditional medicine. User profiles were distinctively different between varied T&CM services. Customized approaches to regulate, develop, and institutionalize specific T&CM services are crucial for fulfilling the unique needs of diverse communities.

Demand for emergency services during the COVID-19 pandemic and disease burden: a case study in Portugal.

Background: The COVID-19 pandemic brought changes in the pattern of care use. A significant increase in the volume of emergencies was expected. However, a significant decrease was observed worldwide. Methods: An observational, analytical and cross-sectional study of all records of emergency episodes of patients aged 18 years or older admitted to the emergency services of the University of Porto Hospital Centre (2018-2022) were analysed. Results: During the pandemic, a significant reduction in emergency episode admissions (up to 40% during lockdowns), an increase in pre-emergency services, and discharges from Infectious Diseases and Internal Medicine was observed. The discharges from General Practice and General Practice and Family Medicine were residual. Conclusion: The lower use and type of use of emergency services during the COVID-19 pandemic had a negative impact on the disease burden. This could be prevented in future pandemics through the development of strategies to promote confidence in the use of health resources and establishing contingency plans for virtual assistance.

Determinants of e-Mental Health Use During COVID-19: Cross-sectional Canadian Study.

BACKGROUND: Access to mental health treatment across Canada remains a challenge, with many reporting unmet care needs. National and provincial e-Mental health (eMH) programs have been developed over the past decade across Canada, with many more emerging during COVID-19 in an attempt to reduce barriers related to geography, isolation, transportation, physical disability, and availability. OBJECTIVE: The aim of this study was to identify factors associated with the utilization of eMH services across Canada during the COVID-19 pandemic using Andersen and Newman's framework of health service utilization. METHODS: This study used data gathered from the 2021 Canadian Digital Health Survey, a cross-sectional, web-based survey of 12,052 Canadians aged 16 years and older with internet access. Bivariate associations between the use of eMH services and health service utilization factors (predisposing, enabling, illness level) of survey respondents were assessed using χ2 tests for categorical variables and t tests for the continuous variable. Logistic regression was used to predict the probability of using eMH services given the respondents' predisposing, enabling, and illness-level factors while adjusting for respondents' age and gender. RESULTS: The proportion of eMH service users among survey respondents was small (883/12,052, 7.33%). Results from the logistic regression suggest that users of eMH services were likely to be those with regular family physician access (odds ratio [OR] 1.57, P=.02), living in nonrural communities (OR 1.08, P<.001), having undergraduate (OR 1.40, P=.001) or postgraduate (OR 1.48, P=.003) education, and being eHealth literate (OR 1.05, P<.001). Those with lower eMH usage were less likely to speak English at home (OR 0.06, P<.001). CONCLUSIONS: Our study provides empirical evidence on the impact of individual health utilization factors on the use of eMH among Canadians during the COVID-19 pandemic. Given the opportunities and promise of eMH services in increasing access to care, future digital interventions should both tailor themselves toward users of these services and consider awareness campaigns to reach nonusers. Future research should also focus on understanding the reasons behind the use and nonuse of eMH services.

Consulting the Oracle: A Delphi study for determining parameters for a mental health user profile and personalization strategy for an online service to aid grieving older adults.

While much effort has been devoted to the development of mental e-health interventions, the tailoring of these applications to user characteristics and needs is a comparatively novel field of research. The premise of personalizing mental e-health interventions is that personalization increases user motivation and (thereby) mitigates intervention dropout and enhances clinical effectiveness. In this study, we selected user profile parameters for personalizing a mental e-health intervention for older adults who lost their spouse. We conducted a three-round Delphi study involving an international and interdisciplinary expert panel (N = 16) with two objectives. The first aim was to elicit adaptation strategies that can be used to dynamically readjust the intervention to the user's needs. The second aim was to identify a set of meaningful indicators for monitoring the user from within the grief intervention to escalate from self-help to blended care, whenever advisable. This Delphi study used as starting point an evaluated, text-based grief intervention composed of ten modules, including psychoeducation about grief and cognitive-behavioral exercises to support the user in adjusting their lives after bereavement. Every user follows this grief intervention in a linear fashion from beginning to end. The resulting conceptual adaptation model encompasses dynamic adjustments, as well as one-time adjustments performed at the initialization of the service. On the level of the application structure, the adaptations affect when which topic module is presented to the user. The adaptations further provide strategies for adjusting the text-based content of individual intervention modules dependent on user characteristics and for selecting appropriate reactions to user input. Eighteen monitoring parameters were elicited and grouped into four categories: clinical, behavioral/emotional, interactive, and external. Parameters that were perceived as most urgent to attend to for escalation were Suicidality, Self-destructive behavior, Client-initiated escalation, Unresponsiveness and (Complicated) Grief symptoms.

DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data.

Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one's opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either 'human' or 'bot.' We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework 'DeeProBot,' which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.

The Digital Divide and Seeking Health Information on Smartphones in Asia: Survey Study of Ten Countries.

BACKGROUND: Although recent developments in mobile health have elevated the importance of how smartphones empower individuals to seek health information, research investigating this phenomenon in Asian countries has been rare. OBJECTIVE: The goal of our study was to provide a comprehensive profile of mobile health information seekers and to examine the individual- and country-level digital divide in Asia. METHODS: With survey data from 10 Asian countries (N=9086), we ran multilevel regression models to assess the effect of sociodemographic factors, technological factors, and country-level disparities on using smartphones to seek health information. RESULTS: Respondents who were women (ß=.13, P<.001), parents (ß=.16, P<.001), employed (ß=.08, P=.002), of higher social status (ß=.08, P<.001), and/or from countries with low health expenditures (ß=.19, P=.02) were more likely to use smartphones to seek health information. In terms of technological factors, technology innovativeness (ß=.10, P<.001) and frequency of smartphone use (ß=.42, P<.001) were important factors of health information seeking, whereas the effect of online information quality was marginal (ß=-.04, P<.001). CONCLUSIONS: Among smartphone users in Asia, health information seeking varies according to individuals' socioeconomic status, their innovativeness toward technology, and their frequency of smartphone use. Although smartphones widen the digital divide among individuals with different socioeconomic status, they also bridge the divide between countries with varying health expenditures. Smartphones appear to be a particularly useful complement to manage health in developing countries.

No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway.

The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. The results show that k-medoids provides the values of F1-measure up to 0.91% for SI and UPL tasks. Moreover, the results prove the effectiveness of the methods which tackle the loss of image details through the compression process on the SNs, even for the images from the same model of smartphones. An important outcome of our work is presenting the inter-layer UPL task, which is more desirable in digital investigations as it can link user profiles on different SNs.

Mixed Assessment of Virtual Serious Games Applied in Architectural and Urban Design Education.

The creation and usage of serious games on virtual reality (VR) and/or interactive platforms for the teaching of architecture, construction, urban planning, and other derived areas, such as security and risk prevention, require design processes, studies, and research that lead to further consolidation expansion. In that sense, this paper presents two main aims developed: the improvement of a virtual navigation system through the results of previous user studies and mixed research (quantitative and qualitative) improved based on the user perception for educational and professional uses. The VR system used is based on Unreal Engine programming of the HTC Vive sensor. This study is related to the GAME4City 3.0 and a broader project focused on gamified visualization and its educational uses in architectural and urban projects. The results reflect great interest, good usability, and high motivation for further usage for all types of users. However, an apparent resistance to deepen its use continues to be perceived in academia. Based on the research results, weak points of educational gamified systems have been identified, and the main differences and needs in user profiles' function. With these data, progress regarding implementing this kind of system at the teaching and professional levels must be pursued.

Clustering Users to Determine the Most Suitable Gamification Elements.

The use of gamification elements has extended from being a complement for a product to being integrated into multiple public services to motivate the user. The first drawback for service designers is choosing which gamification elements are appropriate for the intended audience, in addition to the possible incompatibilities between gamification elements. This work proposes a clustering technique that enables mapping different user profiles in relation to their preferred gamification elements. Additionally, by mapping the best cluster for each gamification element, it is possible to determine the preferred game genre. The article answered the following research questions: What is the relationship between the genre of the game and the element of gamification? Different user groups (profiles) for each gamification element? Results indicate that there are cases where the users are divided between those who agree or disagree. However, other elements present a great heterogeneity in the number of groups and the levels of agreement.

Preferencias del usuario, categorización de consultas y documentos, tres variables importantes en el cálculo de relevancia / User preferences, query and document categorization, three important variables in relevance calculation

The quality of an information retrieval system depends largely on the satisfaction degree of users with the results obtained when executing a query, so it is essential to design processes that store the preferences patterns of each of them and vary the way in which the results are shown taking into account the specific characteristics of each user. The objective of this article was to present an algorithm for calculating the relevance of the documents provided to users, which used the variables: the user's search profile, the category of the documents and the category of the query as parameters, to customize the results provided by the search engine to the users. In addition, it used as impulse factors the degree of predominance of a search category in the user's profile and the categories to which the document belongs. To validate the algorithm, precision and recall metrics were applied to check that the results obtained are relevant to users(AU)

La calidad de un sistema de recuperación de información depende en gran medida del grado de satisfacción de los usuarios en cuanto a los resultados obtenidos al realizar una consulta. Para obtener resultados de búsquedas relevantes es esencial diseñar procesos que almacenen los patrones de preferencias de cada usuario. Este estudio tuvo como objetivo presentar un algoritmo para el cálculo de la relevancia de los documentos brindados. El algoritmo utilizó como parámetros las siguientes variables: perfil de búsqueda del usuario, categoría de los documentos y categoría de la consulta para personalizar los resultados brindados mediante el motor de búsqueda. Además, utilizó como factores de impulso el grado de predominio de una categoría de búsqueda en el perfil del usuario y en las categorías a las que pertenece el documento. Para la validación del modelo se aplicaron las métricas de precisión y exhaustividad que permitieron comprobar que los resultados obtenidos son relevantes para los consumidores de la información(AU)