Younger adults have difficulties identifying emotional facial expressions from faces covered by face masks. It is important to evaluate how face mask wearing might specifically impact older people, because they have lower emotion identification performance than younger adults, even without face masks. We compared performance of 62 young and 38 older adults in an online task of emotional facial expression identification using masked or unmasked pictures of faces with fear, happiness, anger, surprise, and neutral expression, from different viewpoints. Face masks affected performance in both age groups, but more so in older adults, specifically for negative emotions (anger, fear), in favour of the saliency hypothesis as an explanation for the positive advantage. Additionally, face masks more affected emotion recognition on profile than on three-quarter or full-face views. Our results encourage using clearer and full-face expressions when dealing with older people while wearing face masks.

Speed of visual object recognition is facilitated after active manual exploration of objects relative to passive visual processing alone. Manual exploration allows viewers to select important information about object structure that may facilitate recognition. Viewpoints where the objects\' axis of elongation is perpendicular or parallel to the line of sight are selected more during exploration, recognized faster than other viewpoints, and afford the most information about structure when object movement is controlled by the viewer. Prior work used virtual object exploration in active and passive viewing conditions, limiting multisensory structural object information. Adding multisensory information to encoding may change accuracy of overall recognition, viewpoint selection, and viewpoint recognition. We tested whether the known active advantage for object recognition would change when real objects were studied, affording visual and haptic information. Participants interacted with 3D novel objects during manual exploration or passive viewing of another\'s object interactions. Object recognition was tested using several viewpoints of rendered objects. We found that manually explored objects were recognized more accurately than objects studied through passive exploration and that recognition of viewpoints differed from previous work.

ChatGPT (OpenAI) is an advanced natural language processing tool with growing applications across various disciplines in medical research. Thematic analysis, a qualitative research method to identify and interpret patterns in data, is one application that stands to benefit from this technology. This viewpoint explores the use of ChatGPT in three core phases of thematic analysis within a medical context: (1) direct coding of transcripts, (2) generating themes from a predefined list of codes, and (3) preprocessing quotes for manuscript inclusion. Additionally, we explore the potential of ChatGPT to generate interview transcripts, which may be used for training purposes. We assess the strengths and limitations of using ChatGPT in these roles, highlighting areas where human intervention remains necessary. Overall, we argue that ChatGPT can function as a valuable tool during analysis, enhancing the efficiency of the thematic analysis and offering additional insights into the qualitative data. While ChatGPT may not adequately capture the full context of each participant, it can serve as an additional member of the analysis team, contributing to researcher triangulation through knowledge building and sensemaking.

The collection of sexual orientation in routine data, generated either from contacts with health services or in infrastructure data resources designed and collected for policy and research, has improved substantially in the United Kingdom in the last decade. Inclusive measures of gender and transgender status are now also beginning to be collected. This viewpoint considers current data collections, and their strengths and limitations, including accessing data, sample size, measures of sexual orientation and gender, measures of health outcomes, and longitudinal follow-up. The available data are considered within both sociopolitical and biomedical models of health for individuals who are lesbian, gay, bisexual, transgender, queer, or of other identities including nonbinary (LGBTQ+). Although most individual data sets have some methodological limitations, when put together, there is now a real depth of routine data for LGBTQ+ health research. This paper aims to provide a framework for how these data can be used to improve health and health care outcomes. Four practical analysis approaches are introduced-descriptive epidemiology, risk prediction, intervention development, and impact evaluation-and are discussed as frameworks for translating data into research with the potential to improve health.

Recognizing faces regardless of their viewpoint is critical for social interactions. Traditional theories hold that view-selective early visual representations gradually become tolerant to viewpoint changes along the ventral visual hierarchy. Newer theories, based on single-neuron monkey electrophysiological recordings, suggest a three-stage architecture including an intermediate face-selective patch abruptly achieving invariance to mirror-symmetric face views. Human studies combining neuroimaging and multivariate pattern analysis (MVPA) have provided convergent evidence of view selectivity in early visual areas. However, contradictory conclusions have been reached concerning the existence in humans of a mirror-symmetric representation like that observed in macaques. We believe these contradictions arise from low-level stimulus confounds and data analysis choices. To probe for low-level confounds, we analyzed images from two face databases. Analyses of image luminance and contrast revealed biases across face views described by even polynomials-i.e., mirror-symmetric. To explain major trends across neuroimaging studies, we constructed a network model incorporating three constraints: cortical magnification, convergent feedforward projections, and interhemispheric connections. Given the identified low-level biases, we show that a gradual increase of interhemispheric connections across network-layers is sufficient to replicate view-tuning in early processing stages and mirror-symmetry in later stages. Data analysis decisions-pattern dissimilarity measure and data recentering-accounted for the inconsistent observation of mirror-symmetry across prior studies. Pattern analyses of human fMRI data (of either sex) revealed biases compatible with our model. The model provides a unifying explanation of MVPA studies of viewpoint selectivity and suggests observations of mirror-symmetry originate from ineffectively normalized signal imbalances across different face views.

As wearable devices, which allow individuals to track and self-manage their health, become more ubiquitous, the opportunities are growing for researchers to use these sensors within interventions and for data collection. They offer access to data that are captured continuously, passively, and pragmatically with minimal user burden, providing huge advantages for health research. However, the growth in their use must be coupled with consideration of their potential limitations, in particular, digital inclusion, data availability, privacy, ethics of third-party involvement, data quality, and potential for adverse consequences. In this paper, we discuss these issues and strategies used to prevent or mitigate them and recommendations for researchers using wearables as part of interventions or for data collection.

Sri Lanka is representative of challenges faced by low-income and middle-income countries, including the rise in the prevalence of autism and the lack of sufficient autism-specific services in the state sectors. The experience in establishing a Center to provide services for children with autism in Northern Sri Lanka is described. Funding and resourcing were accessed through an innovative partnership-based public/non-governmental organisation/charity model, where service-based outcomes were the main objectives. This model, incorporating state institutions, local and international charity organisations, and volunteers, devised a bespoke approach to care provision using the available resources under the clinical supervision of a consultant psychiatrist and the administrative purview of the Regional Director of Health Services. The evolution of this Center into a Learning Health System is described, reflecting how a minimalistic partnership approach focused on the integration of existing organisations and services could be a feasible model for the delivery of high-quality healthcare in low-resource settings.

With the rapid development of science, technology, and engineering, large amounts of data have been generated in many fields in the past 20 years. In the process of medical research, data are constantly generated, and large amounts of real-world data form a \"data disaster.\" Effective data analysis and mining are based on data availability and high data quality. The premise of high data quality is the need to clean the data. Data cleaning is the process of detecting and correcting \"dirty data,\" which is the basis of data analysis and management. Moreover, data cleaning is a common technology for improving data quality. However, the current literature on real-world research provides little guidance on how to efficiently and ethically set up and perform data cleaning. To address this issue, we proposed a data cleaning framework for real-world research, focusing on the 3 most common types of dirty data (duplicate, missing, and outlier data), and a normal workflow for data cleaning to serve as a reference for the application of such technologies in future studies. We also provided relevant suggestions for common problems in data cleaning.

In the wake of the COVID-19 pandemic, video consultation was introduced in general practice in many countries around the world as a solution to provide remote health care to patients. It was assumed that video consultation would find widespread adoption in post-COVID-19 general practice. However, adoption rates remain low across countries in Northern Europe, suggesting that barriers to its use exist among general practitioners and other practice staff. In this viewpoint, we take a comparative approach, reflecting on similarities and differences in implementation conditions of video consultations in 5 Northern European countries\' general practice settings that might have created barriers to its use within general practice. We convened at a cross-disciplinary seminar in May 2022 with researchers and clinicians from 5 Northern European countries with expertise in digital care in general practice, and this viewpoint emerged out of dialogues from that seminar. We have reflected on barriers across general practice settings in our countries, such as lacking technological and financial support for general practitioners, that we feel are critical for adoption of video consultation in the coming years. Furthermore, there is a need to further investigate the contribution of cultural elements, such as professional norms and values, to adoption. This viewpoint may inform policy work to ensure that a sustainable level of video consultation use can be reached in the future, one that reflects the reality of general practice settings rather than policy optimism.

UNASSIGNED: The assessment and management of misophonia need a team approach, and audiologists are essential team members. However, the role of an audiologist in this condition is not well understood, and there is a lack of awareness even among professionals about their role in the assessment and management of misophonia.
UNASSIGNED: The main aim of our study is to document the present level of awareness and knowledge about misophonia assessment and management among audiologists in India.
UNASSIGNED: A descriptive cross-sectional study was carried out among audiologists from all over India. Descriptive statistical procedures were measured based on the type of questions being addressed, and a non-parametric chi-square test was done to see the association among variables.
UNASSIGNED: The results show a lack of knowledge about misophonia even among audiologists, as only 15.3% of the audiologist reported being confident in handling cases with misophonia.
UNASSIGNED: Although the exact assessment and management of misophonia is still the topic of debate, it is clear that audiologists are the team\'s key members. However, the results clearly show a lack of confidence in handling cases of misophonia among audiologists in India. This result shows the future need for Research in misophonia from an audiological perspective.