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Indigenous youth mental health is an urgent public health issue, which cannot be addressed with a one-size-fits-all approach. The success of health policies in Indigenous communities is dependent on bottom-up, culturally appropriate, and strengths-based prevention strategies. In order to maximize the effectiveness of these strategies, they need to be embedded in replicable and contextually relevant mechanisms such as school curricula across multiple communities. Moreover, to engage youth in the twenty-first century, especially in rural and remote areas, it is imperative to leverage ubiquitous mobile tools that empower Indigenous youth and facilitate novel Two-Eyed Seeing solutions. Smart Indigenous Youth is a 5-year community trial, which aims to improve Indigenous youth mental health by embedding a culturally appropriate digital health initiative into school curricula in rural and remote Indigenous communities in Canada. This policy analysis explores the benefits of such upstream initiatives. More importantly, this article describes evidence-based strategies to overcome barriers to implementation through the integration of citizen science and community-based participatory research action.
Advances in cancer management have been associated with an increased incidence of emergency presentations with disease- or treatment-related complications.
This study aimed to measure the ability of patients and members of their social network to complete checklists for complications of systemic treatment for cancer and examine the impact on patient-centered and health-economic outcomes.
A prospective interventional cohort study was performed to assess the impact of a smartphone app used by patients undergoing systemic cancer therapy and members of their network to monitor for common complications. The app was used by patients, a nominated "safety buddy," and acute oncology services. The control group was made up of patients from the same institution. Measures were based on process (completion of checklists over 60 days), patient experience outcomes (Hospital Anxiety and Depression Scale and the General version of the Functional Assessment of Cancer Therapy at baseline, 1 month, and 2 months) and healthcklists for common complications of systemic treatments and take an active role in systems supporting their own safety. selleck products A larger sample size will be needed to assess the impact on clinical outcomes and health economics.
Patients undergoing treatment for cancer and their close contacts can complete checklists for common complications of systemic treatments and take an active role in systems supporting their own safety. A larger sample size will be needed to assess the impact on clinical outcomes and health economics.
Voice assistants allow users to control appliances and functions of a smart home by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants' actual motor, linguistic, and cognitive abilities.
The aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home).
A group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of prediccognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities.
Digital health innovations are being prioritized on international policy agendas in the hope that they will help to address the existing health system challenges.
The aim of this study was to explore the setup, design, facilities, and strategic priorities of leading United Kingdom and United States health care innovation centers to identify transferable lessons for accelerating their creation and maximizing their impact.
We conducted qualitative case studies consisting of semistructured, audio-recorded interviews with decision makers and center staff in 6 innovation centers. We also conducted nonparticipant observations of meetings and center tours, where we took field notes. Qualitative data were analyzed initially within and then across cases facilitated by QSR International's NVivo software.
The centers had different institutional arrangements, including university-associated institutes or innovation laboratories, business accelerators or incubators, and academic health science partnership models. e support structures, including the development of new career pathways.
Although innovation in health care settings is unpredictable, we offer insights that may help those establishing innovation centers. The key in this respect is the ability to support different kinds of innovations at different stages through adequate support structures, including the development of new career pathways.
Supervised machine learning (ML) is being featured in the health care literature with study results frequently reported using metrics such as accuracy, sensitivity, specificity, recall, or F1 score. Although each metric provides a different perspective on the performance, they remain to be overall measures for the whole sample, discounting the uniqueness of each case or patient. Intuitively, we know that all cases are not equal, but the present evaluative approaches do not take case difficulty into account.
A more case-based, comprehensive approach is warranted to assess supervised ML outcomes and forms the rationale for this study. This study aims to demonstrate how the item response theory (IRT) can be used to stratify the data based on how difficult each case is to classify, independent of the outcome measure of interest (eg, accuracy). This stratification allows the evaluation of ML classifiers to take the form of a distribution rather than a single scalar value.
Two large, public intensive care unit data sets, Medical Information Mart for Intensive Care III and electronic intensive care unit, were used to showcase this method in predicting mortality.
