Purpose For most people, nursing homes are a place they reside for the rest of their lives. Therefore, a nursing home, apart from providing good care, has to provide for a sense of home (1). Mostly professionals are responsible for this sense of home. The aim of this study was to explore how professionals (both care professionals, managers, suppliers of nursing home equipment and architects experience 'sense of home' in a nursing home. Method Over 70 persons (±20-65 years old, 2/3 women) having varying professional involvement in nursing homes participated. The Lego serious play method was used to engage participants in a personal and authentic manner (2). After building their model of 'sense of home', participants recorded their main issues on a sticky note. The models represented the personal stories of the participants, who shared the meaning of their model. Results & Discussion The findings revealed that a range of themes were considered important by the participants (table 1). The main themes involved privacy, space, mobility and freedom, for instance, to open doors, social engagement, being recognised as the person used to before, and nature or the presence of plants and animals. These results are compared with results of a recently performed literature review and photography supported research in residents, family and staff in four nursing homes (3).
Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user’s perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human- AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.
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Background: Peer review is at the heart of the scientific process. With the advent of digitisation, journals started to offer electronic articles or publishing online only. A new philosophy regarding the peer review process found its way into academia: the open peer review. Open peer review as practiced by BioMed Central (BMC) is a type of peer review where the names of authors and reviewers are disclosed and reviewer comments are published alongside the article. A number of articles have been published to assess peer reviews using quantitative research. However, no studies exist that used qualitative methods to analyse the content of reviewers’ comments. Methods: A focused mapping review and synthesis (FMRS) was undertaken of manuscripts reporting qualitative research submitted to BMC open access journals from 1 January – 31 March 2018. Free-text reviewer comments were extracted from peer review reports using a 77-item classification system organised according to three key dimensions that represented common themes and sub-themes. A two stage analysis process was employed. First, frequency counts were undertaken that allowed revealing patterns across themes/sub-themes. Second, thematic analysis was conducted on selected themes of the narrative portion of reviewer reports. Results: A total of 107 manuscripts submitted to nine open-access journals were included in the FMRS. The frequency analysis revealed that among the 30 most frequently employed themes “writing criteria” (dimension II) is the top ranking theme, followed by comments in relation to the “methods” (dimension I). Besides that, some results suggest an underlying quantitative mindset of reviewers. Results are compared and contrasted in relation to established reporting guidelines for qualitative research to inform reviewers and authors of frequent feedback offered to enhance the quality of manuscripts. Conclusions: This FMRS has highlighted some important issues that hold lessons for authors, reviewers and editors. We suggest modifying the current reporting guidelines by including a further item called “Degree of data transformation” to prompt authors and reviewers to make a judgment about the appropriateness of the degree of data transformation in relation to the chosen analysis method. Besides, we suggest that completion of a reporting checklist on submission becomes a requirement.
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Chronische gewrichtsaandoeningen zijn veelvoorkomende aandoeningen waarmee patiënten bij de fysiotherapeut of oefentherapeut komen. Aandoeningen zoals artrose en reuma veroorzaken problemen in het dagelijks functioneren vanwege pijn en verminderde mobiliteit. Genezing is vaak niet mogelijk, maar het bevorderen van zelfmanagement kan verergering voorkomen. Oefentherapeuten en fysiotherapeuten spelen een centrale rol in het ondersteunen van zelfmanagement bij patiënten met gewrichtsaandoeningen. De inzet van online toepassingen, waaronder mobiele applicaties, en online platforms, die gericht zijn op het bevorderen van zelfmanagement (in dit voorstel gedefinieerd als Behavioral Intervention Technologies: BITs) kunnen patiënten met chronische gewrichtsaandoeningen ondersteunen. Echter, voor veel professionals is het onduidelijk hoe BITs kunnen worden ingezet om zelfmanagement te vergroten en hoe dit gecombineerd kan worden met fysieke begeleiding. Daarom onderzoeken we in dit tweejarige project de manier waarop oefen- en fysiotherapeuten coaching op zelfmanagement via BITs kunnen vormgeven. In werkpakket 1 brengen we met een review, observaties en een concept mapping in kaart welke elementen en randvoorwaarden van BITs belangrijk zijn voor het bevorderen van zelfmanagement. Zodra we inzicht hebben in deze elementen en randvoorwaarden wordt in co-creatie met stakeholders toegewerkt naar beroepsrollen en beroepscompetenties die voorwaardelijk zijn voor het gebruik van BITs. Met de input van deze onderzoeksactiviteiten ontwikkelen we samen met de doelgroep de AmSOS methodiek die professionals helpt bij het gebruik van BITs om zelfmanagement te bevorderen bij patiënten met chronische gewrichtsaandoeningen (WP2). Om te bepalen in hoeverre de methodiek bruikbaar is in de praktijk wordt in WP3 een haalbaarheidsstudie opgezet waarbij 25 eerstelijnsfysio- en/of oefentherapiepraktijken de AmSOS methodiek gaan gebruiken in de behandeling van patiënten met chronische gewrichtsaandoeningen. Omdat gewrichtsaandoeningen een substantieel onderdeel zijn van de curricula, maar tegelijkertijd weinig aandacht wordt besteed aan technologie en zelfmanagement, ontwikkelen we in WP4 een onderwijsmodule voor scholing van studenten en praktiserende oefen- en fysiotherapeuten.
Artificial Intelligence (AI) wordt realiteit. Slimme ICT-producten die diensten op maat leveren accelereren de digitalisering van de maatschappij. De grote innovaties van de komende jaren –zelfrijdende auto’s, spraakgestuurde virtuele assistenten, autodiagnose systemen, robots die autonoom complexe taken uitvoeren – zijn datagedreven en hebben een AI-component. Dit gaat de rol van professionals in alle domeinen, gezondheidzorg, bouwsector, financiële dienstverlening, maakindustrie, journalistiek, rechtspraak, etc., raken. ICT is niet meer volgend en ondersteunend (een ‘enabling’ technologie), maar de motor die de transformatie van de samenleving in gang zet. Grote bedrijven, overheidsinstanties, het MKB, en de vele startups in de Brainport regio zijn innovatieve datagedreven scenario’s volop aan het verkennen. Dit wordt nog eens versterkt door de democratisering van AI; machine learning en deep learning algoritmes zijn beschikbaar zowel in open source software als in Cloud oplossingen en zijn daarmee toegankelijk voor iedereen. Data science wordt ‘applied’ en verschuift van een PhD specialisme naar een HBO-vaardigheid. Het stadium waarin veel bedrijven nu verkeren is te omschrijven als: “Help, mijn AI-pilot is succesvol. Wat nu?” Deze aanvraag richt zich op het succesvol implementeren van AI binnen de context van softwareontwikkeling. De onderzoeksvraag van dit voorstel is: “Hoe kunnen we state-of-the-art data science methoden en technieken waardevol en verantwoord toepassen ten behoeve van deze slimme lerende ICT-producten?” De postdoc gaat fungeren als een linking pin tussen alle onderzoeksprojecten en opdrachten waarbij studenten ICT-producten met AI (machine learning, deep learning) ontwikkelen voor opdrachtgevers uit de praktijk. Door mee te kijken en mee te denken met de studenten kan de postdoc overzicht en inzicht creëren over alle cases heen. Als er overzicht is kan er daarna ook gestuurd worden op de uit te voeren cases om verschillende deelaspecten samen met de studenten te onderzoeken. Deliverables zijn rapporten, guidelines en frameworks voor praktijk en onderwijs, peer-reviewed artikelen en kennisdelingsevents.
Physical rehabilitation programs revolve around the repetitive execution of exercises since it has been proven to lead to better rehabilitation results. Although beginning the motor (re)learning process early is paramount to obtain good recovery outcomes, patients do not normally see/experience any short-term improvement, which has a toll on their motivation. Therefore, patients find it difficult to stay engaged in seemingly mundane exercises, not only in terms of adhering to the rehabilitation program, but also in terms of proper execution of the movements. One way in which this motivation problem has been tackled is to employ games in the rehabilitation process. These games are designed to reward patients for performing the exercises correctly or regularly. The rewards can take many forms, for instance providing an experience that is engaging (fun), one that is aesthetically pleasing (appealing visual and aural feedback), or one that employs gamification elements such as points, badges, or achievements. However, even though some of these serious game systems are designed together with physiotherapists and with the patients’ needs in mind, many of them end up not being used consistently during physical rehabilitation past the first few sessions (i.e. novelty effect). Thus, in this project, we aim to 1) Identify, by means of literature reviews, focus groups, and interviews with the involved stakeholders, why this is happening, 2) Develop a set of guidelines for the successful deployment of serious games for rehabilitation, and 3) Develop an initial implementation process and ideas for potential serious games. In a follow-up application, we intend to build on this knowledge and apply it in the design of a (set of) serious game for rehabilitation to be deployed at one of the partners centers and conduct a longitudinal evaluation to measure the success of the application of the deployment guidelines.
