Elena V. Syurina

Across European social work, it is generally assumed that the professional autonomy is a prerequisite for providing good care. However, in the Netherlands, different channels are promoting moves away from autonomy and argue that social workers should practise ‘professional disobedience’: resisting policies or protocols and ‘getting things done’ despite rules or bureaucracy, promoted as a call for action and reform. In this article, we explore this concept through in-depth interviews with Dutch professionals, managers and administrators. We find that although professional autonomy and disobedience are often framed as an individual endeavour, they are practised across social work hierarchies as a collective practice that requires a shared, situational and moral responsibility embedded in a reflexive and responsive governance system. By framing it as professional ‘disobedience’ and a practice of resistance, systemic flaws can be highlighted, though only if this information is fed back into policy to guide change.

Background: Experience-based knowledge and value considerations of health professionals, citizens, and patients are essential to formulate public health and clinical guidelines that are relevant and applicable to medical practice. Conventional methods for incorporating such knowledge into guideline development often involve a limited number of representatives and are considered to be time-consuming. Including experiential knowledge can be crucial during rapid guidance production in response to a pandemic but it is difficult to accomplish. Objective: This proof-of-concept study explored the potential of artificial intelligence (AI)–based methods to capture experiential knowledge and value considerations from existing data channels to make these insights available for public health guideline development. Methods: We developed and examined AI-based methods in relation to the COVID-19 vaccination guideline development in the Netherlands. We analyzed Dutch messages shared between December 2020 and June 2021 on social media and on 2 databases from the Dutch National Institute for Public Health and the Environment (RIVM), where experiences and questions regarding COVID-19 vaccination are reported. First, natural language processing (NLP) filtering techniques and an initial supervised machine learning model were developed to identify this type of knowledge in a large data set. Subsequently, structural topic modeling was performed to discern thematic patterns related to experiences with COVID-19 vaccination. Results: NLP methods proved to be able to identify and analyze experience-based knowledge and value considerations in large data sets. They provide insights into a variety of experiential knowledge that is difficult to obtain otherwise for rapid guideline development. Some topics addressed by citizens, patients, and professionals can serve as direct feedback to recommendations in the guideline. For example, a topic pointed out that although travel was not considered as a reason warranting prioritization for vaccination in the national vaccination campaign, there was a considerable need for vaccines for indispensable travel, such as cross-border informal caregiving, work or study, or accessing specialized care abroad. Another example is the ambiguity regarding the definition of medical risk groups prioritized for vaccination, with many citizens not meeting the formal priority criteria while being equally at risk. Such experiential knowledge may help the early identification of problems with the guideline’s application and point to frequently occurring exceptions that might initiate a revision of the guideline text. Conclusions: This proof-of-concept study presents NLP methods as viable tools to access and use experience-based knowledge and value considerations, possibly contributing to robust, equitable, and applicable guidelines. They offer a way for guideline developers to gain insights into health professionals, citizens, and patients’ experience-based knowledge, especially when conventional methods are difficult to implement. AI-based methods can thus broaden the evidence and knowledge base available for rapid guideline development and may therefore be considered as an important addition to the toolbox of pandemic preparedness.

Background: The number of autistic individuals attending university and entering the workforce is growing, but there is a persistent employment gap. Higher education careers services offer students help to secure employment post-graduation. This research sought to identify barriers to and facilitators of success with regards to career advisors’ practice in helping autistic students prepare for job interviews and secure employment. Methods: A mixed-methods participatory research design incorporating surveys, co-creation workshops, and interviews with stakeholders was employed. Results: Quantitative results showed differences in what advisors and students/graduates viewed as the biggest barriers to employment, with students/graduates rating stress and professional qualifications significantly higher and advisors rating interacting with clients/customers significantly higher. Gender differences were also found. Qualitative results revealed the biggest barriers to be inflexible employers, interview stress, diagnostic disclosure, and confidence; facilitators included an individual focus, clear communication, strengths-based approach leading to self-insight, and mock interviews. Conclusions: Practices identified for better serving autistic students included discussing disclosure options, building confidence, reducing stress through a strengths-based approach, and mock interviews, following up to identify autism-friendly employers, focusing on the individual rather than the diagnosis, and communicating clearly with students/graduates.

The chapter starts by showing how, from Articles 12 and 24 of the Convention on the Rights of the Child and Articles 12 and 25 of the Convention on the Rights

BACKGROUND Experience-based knowledge and value considerations of health professionals, citizens, and patients are essential to formulate public health and clinical guidelines that are relevant and applicable to medical practice. Conventional methods for incorporating such knowledge into guideline development often involve a limited number of representatives and are considered to be time-consuming. Including experiential knowledge can be crucial during rapid guidance production in response to a pandemic but it is difficult to accomplish. OBJECTIVE This proof-of-concept study explored the potential of artificial intelligence (AI)–based methods to capture experiential knowledge and value considerations from existing data channels to make these insights available for public health guideline development. METHODS We developed and examined AI-based methods in relation to the COVID-19 vaccination guideline development in the Netherlands. We analyzed Dutch messages shared between December 2020 and June 2021 on social media and on 2 databases from the Dutch National Institute for Public Health and the Environment (RIVM), where experiences and questions regarding COVID-19 vaccination are reported. First, natural language processing (NLP) filtering techniques and an initial supervised machine learning model were developed to identify this type of knowledge in a large data set. Subsequently, structural topic modeling was performed to discern thematic patterns related to experiences with COVID-19 vaccination. RESULTS NLP methods proved to be able to identify and analyze experience-based knowledge and value considerations in large data sets. They provide insights into a variety of experiential knowledge that is difficult to obtain otherwise for rapid guideline development. Some topics addressed by citizens, patients, and professionals can serve as direct feedback to recommendations in the guideline. For example, a topic pointed out that although travel was not considered as a reason warranting prioritization for vaccination in the national vaccination campaign, there was a considerable need for vaccines for indispensable travel, such as cross-border informal caregiving, work or study, or accessing specialized care abroad. Another example is the ambiguity regarding the definition of medical risk groups prioritized for vaccination, with many citizens not meeting the formal priority criteria while being equally at risk. Such experiential knowledge may help the early identification of problems with the guideline’s application and point to frequently occurring exceptions that might initiate a revision of the guideline text. CONCLUSIONS This proof-of-concept study presents NLP methods as viable tools to access and use experience-based knowledge and value considerations, possibly contributing to robust, equitable, and applicable guidelines. They offer a way for guideline developers to gain insights into health professionals, citizens, and patients’ experience-based knowledge, especially when conventional methods are difficult to implement. AI-based methods can thus broaden the evidence and knowledge base available for rapid guideline development and may therefore be considered as an important addition to the toolbox of pandemic preparedness.

Technology and screen media has its place in every home, yet the influences of the same are less known. This research aims to explore the vulnerabilities that prompt the mothers to use screen media for their children, prior to a diagnosis of autism for their child. It also aims to explore literature the influence of screen media on speech and language development in children. This study combined semi-structured interviews with 16 mothers of autistic children in Southern India and a scoping literature review that resulted in 24 articles. The literature refers to a positive influence when co-viewing with the child, and it predominantly highlights improvements in speech and not in language. The interviews revealed that screens were used as a means of support, a language and learning development tool, or as a calming technique. Thus, the study shows that the mothers resorted to screen use for their children more out of helplessness, and not as an informed choice. Mothers of autistic children clearly express their vulnerabilities and indicate feelings of being lost without advice, with regard to use of screen-time. This suggests a need for more research into how they can be supported.

Due to barriers in accessing and using healthcare services, a large proportion of the care homeless populations receive comes from informal providers. In Delhi, one such informal programme, called Street Medicine, provides healthcare outreach to homeless communities. Clinical practice guidelines are set to be developed for Street Medicine teams in India and form the object of this research. This study uses a social-ecological model to understand the barriers facing Street Medicine teams and the homeless as they attempt to address the latter’s healthcare needs; coupling it with an analytical approach which situates these barriers as the issues within practice through which standardisation can take place. A qualitative inquiry, comprising three months of observations of Street Medicine outreach and interviews with over 30 key informants, was conducted between April and July 2018. The analysis identified novel barriers to addressing the needs of homeless individuals, which bely a deficit between the design of health and social care systems and the agency homeless individuals possess within this system to influence their health outcomes. These barriers – which include user-dependent technological inscriptions, collaborating with untargeted providers and the distinct health needs of homeless individuals – are the entry points for standardising, or opening up, Street Medicine practices .

This mixed-methods study explored the conversation around orthorexia nervosa (ON) on Instagram from a Russian-speaking perspective. Two quantitative data sources were implemented; a comparative content analysis of posts tagged with #opтopeкcия (n = 234) and #orthorexia (n = 243), and an online questionnaire completed by Russian-speakers (n = 96) sharing ON-related content on Instagram. Additionally, five questionnaire participants were interviewed, four of which identified with having (had) ON. Russian-speakers who share ON-related content on Instagram are primarily female, around their late-twenties, and prefer Instagram over other platforms. They describe people with ON as obsessed with correct eating, rather than healthy or clean eating. Instagram appears to have a dual effect; it has the potential to both trigger the onset of ON and encourage recovery. Positive content encourages a healthy relationship with food, promotes intuitive eating, and spread recovery advice. Harmful content, in turn, emphasizes specific diet and beauty ideals. Russian-speaking users mainly post pictures of food, followed by largely informative text that explains what ON is, and what recovery may look like. Their reasons for posting ON-related content are to share personal experiences, support others in recovery, and raise awareness about ON. Two main target audiences were people unaware of ON and people seeking recovery support. The relationship between ON and social media is not strictly limited to the global north. Thus, it may be valuable to further investigate non-English-speaking populations currently underrepresented in ON research.Level of evidence: Level V, descriptive study.