Background: Due to demographic transitions and budget restraints, it is now necessary to search for comprehensive new strategies, in order to constitute a sustainable healthcare system. Recently, various online care platforms for community-dwelling older adults were introduced in several European countries. These platforms have aimed at solidifying social cohesion in the community, so as to support the older adults in coordinating or managing their care and to enhance the self-reliance of these older adults. Consequently, these platforms might contribute to a more sustainable healthcare system. The main research question of this study was twofold: Which online care platforms for older adults are available in the Netherlands and what are their characteristics? Methods: The researchers have performed a scoping review of the online care platforms in the Netherlands, according to the six steps of Arksey & O’Malley (2005), which were as follows: (1) Identifying the research question; (2) Identifying any relevant studies; (3) Selecting the studies; (4) Charting the data; (5) Collating, summarising and reporting on the results; together with (6) consultations with the relevant stakeholders. The study searched for evidence in online scientific databases (Phase 1) and on the Internet (Phase 2). The relevant studies that were published between February 2012 and October 2017 were included. Results: The review resulted in an overview of 21 care platforms, for which 3 types were identified: (1) Community Care Platforms; (2) Care Network Platforms; and (3) System Integrator Platforms. Conclusion: This typology of platforms can guide users – for instance, older adults, care professionals, informal caregivers and municipalities, in choosing a suitable care platform, i.e. the typology gives users insight into the functionalities, goals and target groups which allows them to choose a platform that matches their needs. As far as the authors know, no studies have previously reported on the effects of the online care platforms for older adults in the Netherlands, so further research is required on their impacts and on their benefits.
Previous research shows that automatic tendency to approach alcohol plays a causal role in problematic alcohol use and can be retrained by Approach Bias Modification (ApBM). ApBM has been shown to be effective for patients diagnosed with alcohol use disorder (AUD) in inpatient treatment. This study aimed to investigate the effectiveness of adding an online ApBM to treatment as usual (TAU) in an outpatient setting compared to receiving TAU with an online placebo training. 139 AUD patients receiving face-to-face or online treatment as usual (TAU) participated in the study. The patients were randomized to an active or placebo version of 8 sessions of online ApBM over a 5-week period. The weekly consumed standard units of alcohol (primary outcome) was measured at pre-and post-training, 3 and 6 months follow-up. Approach tendency was measured pre-and-post ApBM training. No additional effect of ApBM was found on alcohol intake, nor other outcomes such as craving, depression, anxiety, or stress. A significant reduction of the alcohol approach bias was found. This research showed that approach bias retraining in AUD patients in an outpatient treatment setting reduces the tendency to approach alcohol, but this training effect does not translate into a significant difference in alcohol reduction between groups. Explanations for the lack of effects of ApBM on alcohol consumption are treatment goal and severity of AUD. Future ApBM research should target outpatients with an abstinence goal and offer alternative, more user-friendly modes of delivering ApBM training.
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In an image-saturated society, methods for visual analysis gain urgency. This special issue explores visual ways to study online images, focusing on their collection and circulation. The proposition we make is to stay as close to the material as possible. How to approach the visual with the visual? What type of images may one design to make sense of, reshape, and reanimate online image collections? How may arrangements of online images promote various analytical procedures, participatory actions, and design interventions? Furthermore, we focus on the role that algorithmic tools, including machine vision, can play in such research efforts while being sensitive to their flaws and shortcomings. Which kinds of collaborations between humans and machines can we envision to better grasp and critically interrogate the dynamics of today’s digital visual culture? The different practices and formats discussed in this special issue (including data feminism, visual scores, machine vision, image networks, field guides) offer a range of approaches that seek to understand, reanimate, and change perspectives on our digital visual culture.
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