Background: Patient participation in goal setting is important to deliver client-centered care. In daily practice, however, patient involvement in goal setting is not optimal. Patient-specific instruments, such as the Patient Specific Complaints (PSC) instrument, can support the goal-setting process because patients can identify and rate their own problems. The aim of this study is to explore patients’ experiences with the feasibility of the PSC, in the physiotherapy goal setting. Method: We performed a qualitative study. Data were collected by observations of physiotherapy sessions (n=23) and through interviews with patients (n=23) with chronic conditions in physiotherapy practices. Data were analyzed using directed content analysis. Results: The PSC was used at different moments and in different ways. Two feasibility themes were analyzed. First was the perceived ambiguity with the process of administration: patients perceived a broad range of experiences, such as emotional and supportive, as well as feeling a type of uncomfortableness. The second was the perceived usefulness: patients found the PSC useful for themselves – to increase awareness and motivation and to inform the physiotherapist – as well as being useful for the physiotherapist – to determine appropriate treatment for their personal needs. Some patients did not perceive any usefulness and were not aware of any relation with their treatment. Patients with a more positive attitude toward questionnaires, patients with an active role, and health-literate patients appreciated the PSC and felt facilitated by it. Patients who lacked these attributes did not fully understand the PSC’s process or purpose and let the physiotherapist take the lead. Conclusion: The PSC is a feasible tool to support patient participation in the physiotherapy goal setting. However, in the daily use of the PSC, patients are not always fully involved and informed. Patients reported varied experiences related to their personal attributes and modes of administration. This means that the PSC cannot be used in the same way in every patient. It is perfectly suited to use in a dialogue manner, which makes it very suitable to improve goal setting within client-centered care.
Prompt design can be understood similarly to query design, as a prompt aiming to understand cultural dimensions in visual research, forcing the AI to make sense of ambiguity as a way to understand its training dataset and biases ( Niederer, S. and Colombo, G., ‘Visual Methods for Digital Research’). It moves away from prompting engineering and efforts to make “code-like” prompts that suppress ambiguity and prevent the AI from bringing biases to the surface. Our idea is to keep the ambiguity present in the image descriptions like in natural language and let it flow through different stages (degrees) of the broken telephone dynamics. This way we have less control over the result or selection of the ideal result and more questions about the dynamics implicit in the biases present in the results obtained.Different from textual or mathematical results, in which prompt chains or asking the AI to explain how it got the result might be enough, images and visual methods assisted by AI demand new methods to deal with that. Exploring and developing a new approach for it is the main goal of this research project, particularly interested in possible biases and unexplored patterns in AI’s image affordances.How could we detect small biases in describing images and creating based on descriptions when it comes to AI? What exactly do the words written by AI when describing an image stand for? When detecting a ‘human’ or ‘science’, for example, what elements or archetypes are invisible between prompting, and the image created or described?Turning an AI’s image description into a new image could help us to have a glimpse behind the scenes. In the broken telephone game, small misperceptions between telling and hearing, coding and decoding, produce big divergences in the final result - and the cultural factors in between have been largely studied. To amplify and understand possible biases, we could check how this new image would be described by AI, starting a broken telephone cycle. This process could shed light not just into the gap between AI image description and its capacity to reconstruct images using this description as part of prompts, but also illuminate biases and patterns in AI image description and creation based on description.It is in line with previous projects on image clustering and image prompt analysis (see reference links), and questions such as identification of AI image biases, cross AI models analysis, reverse engineering through prompts, image clustering, and analysis of large datasets of images from online image and video-based platforms.The experiment becomes even more relevant in light of the results from recent studies (Shumailov et al., 2024) that show that AI models trained on AI generated data will eventually collapse.To frame this analysis, the proposal from Munn, Magee and Arora (2023) titled Unmaking AI Imagemaking introduces three methodological approaches for investigating AI image models: Unmaking the ecosystem, Unmaking the data and Unmaking the outputs.First, the idea of ecosystem was taken for these authors to describe socio-technical implications that surround the AI models: the place where they have been developed; the owners, partners, or supporters; and their interests, goals, and impositions. “Research has already identified how these image models internalize toxic stereotypes (Birnhane 2021) and reproduce forms of gendered and ethnic bias (Luccioni 2023), to name just two issues” (Munn et al., 2023, p. 2).There are also differences between the different models that currently dominate the market. Although Stable Diffusion seems to be the most open due to its origin, when working with images with this model, biases appear even more quickly than in other models. “In this framing, Stable Diffusion becomes an internet-based tool, which can be used and abused by “the people,” rather than a corporate product, where responsibility is clear, quality must be ensured, and toxicity must be mitigated” (Munn et al., 2023, p. 5).To unmaking the data, it is important to ask ourselves about the source and interests for the extraction of the data used. According to the description of their project “Creating an Ad Library Political Observatory”, “This project aims to explore diverse approaches to analyze and visualize the data from Meta’s ad library, which includes Instagram, Facebook, and other Meta products, using LLMs. The ultimate goal is to enhance the Ad Library Political Observatory, a tool we are developing to monitor Meta’s ad business.” That is to say, the images were taken from political advertising on the social network Facebook, as part of an observation process that seeks to make evident the investments in advertising around politics. These are prepared images in terms of what is seen in the background of the image, the position and posture of the characters, the visible objects. In general, we could say that we are dealing with staged images. This is important since the initial information that describes the AI is in itself a representation, a visual creation.
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This paper theorizes the spiritual processes of community entrepreneuring as navigating tensions that arise when community-based enterprises (CBEs) emerge within communities and generate socio-economic inequality. Grounded on an ethnographic study of a dairy CBE in rural Malawi, findings reveal that intra-community tensions revolve around the occurrence of ‘bad events’ – mysterious tragedies that, among their multiple meanings, are also framed as witchcraft. Community members prepare for, frame, cope and build collective sustenance from ‘bad events’ by intertwining witchcraft and mundane socio-material practices. Together, these practices reflect the mystery and the ambiguity that surround ‘bad events’ and prevent intra-community tensions from overtly erupting. Through witchcraft, intra-community tensions are channelled, amplified and tamed cyclically as this process first destabilizes community social order and then restabilizes it after partial compensation for socio-economic inequality. Generalizing beyond witchcraft, this spiritual view of community entrepreneuring enriches our understanding of entrepreneuring – meant as organization-creation process in an already organized world – in the context of communities. Furthermore, it sheds light on the dynamics of socio-economic inequality surrounding CBEs, and on how spirituality helps community members to cope with inequality and its effects.
PUNC is een driejarig Erasmus+ project dat middelen en methoden wil maken die bruikbaar zijn voor zowel docenten als studenten om hun PUNC (professionele onzekerheidscompetentie) te ontwikkelen. Dit is belangrijk omdat professionals met onzekerheid worden geconfronteerd in de vluchtige, onzekere, complexe en ambigue professionele wereld waarin ze zich bevinden. Deze onzekerheid kan productief gemaakt worden door middel van PUNC.Doel Het doel van het PUNC-project is om docenten in het hoger onderwijs te voorzien van middelen en methoden om studenten in staat te stellen hun PUNC (professionele onzekerheidscompetentie) te ontwikkelen. Aanpak In toenemende mate moeten professionals opereren in situaties die complex en onzeker zijn. Dit kan bij hen leiden tot gevoelens van onzekerheid. Deze gevoelens kunnen stimuleren tot creativiteit en de behoefte er een schepje boven op te doen. Ze kunnen echter ook leiden tot stress en uitstelgedrag. Het is van groot belang in onderwijs studenten de vaardigheden mee te geven hier mee om te gaan. Het project PUNC heeft als doel om docenten in hoger onderwijs te helpen (aankomend) professionals te trainen productief om te gaan met hun eigen onzekerheid, en wel op Europese schaal. De 6 deelnemende partners zullen docenten en studenten betrekken bij het ontwikkelen van: een handleiding voor docenten voor het ontwerpen van een ‘VUCA’ (Volatility, Uncertainty, Complexity en Ambiguity) leeromgeving; een beschrijving van de PUNC-competentie en een bijbehorende methode om leeruitkomsten te definiëren voor het ontwikkelen van iemands PUNC; een PUNC-toolbox ter ondersteuning van het ontwikkelen van iemands PUNC; een E-portfolio die deze ontwikkeling volgt en in kaart brengt. Hiervoor werken we met internationale literatuur, enquêtes, discussiegroepen, en andere middelen. Deze middelen en andere informatie zijn hieronder te vinden wanneer ze beschikbaar komen. En ook via onze PUNC website en de PUNC pagina op LinkedIn houden we iedereen op de hoogte. Daarnaast hebben we zowel internationale projectbijeenkomsten als landelijke publieksbijeenkomsten georganiseerd waarbij de voortgang en tussenresultaten van het PUNC project zijn gepresenteerd. Voor Nederland was dat tijdens de EAPRIL conferentie in November 2022 in Nijmegen. Resultaten Het PUNC-project maakt: Een handleiding voor docenten voor het ontwerpen van een ‘VUCA’ leeromgeving; Een beschrijving van de PUNC-competentie en een bijbehorende methode om leeruitkomsten te definiëren voor het ontwikkelen van iemands PUNC; Een PUNC-toolbox ter ondersteuning van het ontwikkelen van iemands PUNC; Een E-portfolio die deze ontwikkeling volgt en in kaart brengt. Looptijd 01 september 2020 - 31 augustus 2023 Relevantie/impact Dit project is relevant voor alle onderwijsprofessionals en studenten die hun onzekerheid in leren en werken willen verkennen en productief willen maken. De uitkomsten zullen publiekelijk en gratis ter beschikking worden gesteld. Downloads en links
Cities: Action-perspectives for a climate-proof, drought-resilient, and water-sensitive built environment Recurring droughts severely impacted the Dutch built Environment , causing financial, environmental, and social effects. Climate change and urban developments are expected to aggravate this. Although municipalities recognize drought as critical risk, few have prepared for it. This is due to a lack of understanding of the urban water balance under drought and the vulnerability of urban water use(r)s, ambiguity in role and responsibility, and missing action-perspectives. Thirsty Cities aims to address this by developing, collecting, connecting and delivering in a transdisciplinary approach the needed knowledge, insights, tooling, principles, designs, infrastructures and action-perspectives for a climate-proof, drought-resilient, and water-sensitive built environment.Dorstige Steden: Handelingsperspectieven voor een klimaatbestendige, droogteweerbare, en waterrobuuste bebouwde omgeving.De Nederlandse bebouwde omgeving is herhaaldelijk geraakt door droogte, met financiële, ecologische en maatschappelijke effecten. Klimaatverandering en stedelijke ontwikkelingen zullen het droogte-risico naar verwachting doen toenemen. Alhoewel overheden droogte als een risico erkennen, hebben weinigen zich daarop voorbereid. Gebrek aan inzicht in de stedelijke waterbalans onder droogte, de kwetsbaarheid van stedelijke watergebruikers, onduidelijkheid in rol en verantwoordelijkheid van betrokken actoren, en ontbrekende handelingsperspectieven liggen hieraan ten grondslag. ‘Dorstige Steden’ draagt middels trans-disciplinair onderzoek bij aan een klimaatbestendige, droogteweerbare, en waterrobuuste bebouwde omgeving door de benodigde kennis, inzichten, instrumentaria, principes en ontwerpen te ontwikkelen, verzamelen en verbinden en handelingsperspectieven te formuleren.
