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The emergence of ChatGPT, an AI-powered language model, has sparked numerous debates and discussions. In educational research, scholars have raised significant questions regarding the potential, limitations, and ethical concerns around the use of this technology. While research on the application and implications of ChatGPT in academic settings exists, analysis of the perspectives of high-school students are limited. In this study, we use qualitative content analysis to explore the perspectives of high-school students regarding the integration or ban of ChatGPT in their schools through the lens of the Technology Acceptance Model (TAM2). Data was sourced from students' comments to a New York Times Learning Network article. Findings revealed that students' perceptions about integrating or banning ChatGPT in schools are influenced by their assessments of the technology's usefulness, personal experiences, societal technology trends, and ethical considerations. Our findings suggest that student perspectives in this study align with those of educators and policymakers while also possessing unique perspectives that cater to their specific needs and experiences. Implications emphasize the significance of an inclusive decision-making process around the integration of AI schools in educational contexts, including students alongside other stakeholders.

Introduction"Recidivism" is used ubiquitously in juvenile justice research and typically describes repeat legal contact; however, researchers, policymakers, and clinicians operationalize it in various ways. Despite assuming each measure is a proxy for continued delinquent behavior leading to further legal contact, few have examined the association between youth delinquent behavior and self-reported and official records of legal contact. Furthermore, systemic bias against ethnoracial and gender minoritized youth often results in more harsh treatment by the legal system, which could influence recidivism measurement. Latent variable modeling of legal contact is understudied; thus, it is important to examine the feasibility of measuring this construct as a latent variable, including measurement invariance by gender.MethodsAmong 401 youth ages 12-18 years at first ever court contact, we examined three metrics of legal contact over a 2-year follow-up period: youth-report of arrest, caregiver-report of their adolescent's arrest, and official records of the number of new court charges. We examined between-group differences on each metric based on gender and ethnoracial identity. We then measured: (1) the association between youths' self-reported delinquency and each metric, (2) gender-specific associations between self-reported delinquency and each metric, and (3) gender-based measurement invariance for a latent recidivism variable using confirmatory factor analysis.ResultsYouth were consistent reporters of their own delinquent behavior and prospective legal contact measured by arrests. There were no between-group differences based on gender or ethnoracial identity for any legal contact measures. Delinquency and all legal contact variables were positively intercorrelated for the overall sample and the male subsample. For females, delinquency was not associated with caregiver-reported youth arrest or number of new charges. The latent legal contact variable had unique factor structures for male and female subsamples, suggesting no measurement invariance.DiscussionYouth-reported delinquency at first ever legal contact was most strongly associated with youth-reported arrest during a 2-year follow-up period, followed by caregiver-reported arrest, and the number of new charges. Unique latent variable factor structures for male and female subsamples suggests the inter-relation between legal contact variables is gender-specific. Stakeholders should consider prioritizing youth-reported delinquency since it is most strongly related to prospective youth-reported arrest.

Purpose: This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms. Design/methodology/approach: This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning. Findings: Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students' lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables. Originality/value: Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

The importance of data literacies and the shortage of research surrounding data science in elementary schools motivated this research-practice partnership (RPP) between researchers and teachers from a STEM elementary school. We used a narrative case study methodology to describe the instructional practices of one music teacher who co-designed a data science curricular unit during a summer professional development program and implemented it in her 5th-grade music classroom. Data collected for this study include in-person and video observations, reflective journals, artifacts, and interviews. Findings suggest that this teacher integrated data science literacies into her classroom by supporting multiple avenues for data storytelling and relying on learners' everyday discourse and experiences. Our study details a practical example of implementing data science with non-STEM domains in elementary classrooms.

Abstract Background Persistent pain is a frequent cause of sick leave and work disability in Norway. A return-to-work intervention featuring supported work placements, developed in the UK, demonstrated feasibility, and a return-to-work rate of 20% within 6 months was observed in the sample. We sought to adapt the intervention for delivery in Norway and to confirm feasibility prior to a full-scale trial. Methods In this internal pilot, we used a pragmatic cohort randomised controlled approach with national recruitment in Norway. We recruited people who were unemployed (for at least 1 month), having persistent pain (for at least 3 months), aged between 18 and 64, and wanting to return to work. We initially recruited people to an observational cohort study of the impact of being unemployed with persistent pain. After baseline measurement, we randomly sub-sampled participants to whom we offered the intervention, which featured individual case management and support, work-familiarisation sessions, and the offer of a 6-week part-time unpaid work placement. We assessed recruitment rates (aiming to recruit 66, and sub-sample 17 within 6 months); optimal recruitment pathways; intervention acceptance rates; the feasibility of data collection; using video links for work-familiarisation sessions and remote case manager support. Results The pilot ran from June to November 2022. Of 168 people expressing interest, 94 consented. Recruitment posts on Facebook yielded the most ‘expressions of interest’ (66%, n = 111). After screening for eligibility, we included 55 participants. Of these, 19 were randomised to be offered the intervention. Of these, less than half (n = 8) consented to intervention participation. Remote case manager and work-familiarisation sessions appeared feasible. Following a delay in identifying placements, three participants received offers of work placements, with one starting and completing during the pilot period. Data collection methods were feasible, and no adverse events were reported. Conclusions Recruitment and logistical processes, such as remote management by video link, are feasible. However, delivery of the intervention is challenging. In particular, sourcing placements and the time required for identifying appropriate placements was more challenging than anticipated. A full-scale trial is feasible but will require improvements to the placement identification processes. Trial registration ISRCTN85437524 (Referring to the ReISE trial, of which this internal pilot was a part), Registered 31 of May 2022 https://doi.org/10.1186/ISRCTN85437524 Trial funding Norwegian Research Council.

Developing knowledge in education systems is essential in capacity building. When the intention is to build collective capacity and sustain the capacity in schools, we claim there is a need to understand how organizational knowledge is developed and what form this knowledge may take in the school as an organization. However, theory seems to have put little weight on the development of organizational knowledge. In this article, we draw on two cases to discuss why there is a need for theoretical perspectives that may help conceptualize and aid knowledge development in capacity building. Our contribution is a theoretical model that builds on a graded theory concept, in which theory and practice are understood as entangled in process rather than as dichotomous and static categories. We hope to contribute to an understanding of capacity building that avoids the tyranny of theory and the tyranny of practice, and where teachers' and school leaders' professional theorizing is seen as essential for success.

As big data algorithm usage becomes more ubiquitous, it will become critical for all young people, particularly those from historically marginalized populations, to have a deep understanding of data science that empowers them to enact change in their local communities and globally. In this study, we explore the concept of critical machine learning: integrating machine learning knowledge content with social, ethical, and political effects of algorithms. We modified an intergenerational participatory design approach known as cooperative inquiry to co-design a critical machine learning educational program with and for youth ages 9-13 in two after-school centers in the southern United States. Analyzing data from cognitive interviews, observations, and learner artifacts, we describe the roles of children and researchers as meta-design partners. Our findings suggest that cooperative inquiry and meta-design are suitable frameworks for designing critical machine learning educational environments that reflect children's interests and values. This approach may increase youth engagement around the social, ethical, and political implications of large-scale machine learning algorithm deployment.

Introduction“Recidivism” is used ubiquitously in juvenile justice research and typically describes repeat legal contact; however, researchers, policymakers, and clinicians operationalize it in various ways. Despite assuming each measure is a proxy for continued delinquent behavior leading to further legal contact, few have examined the association between youth delinquent behavior and self-reported and official records of legal contact. Furthermore, systemic bias against ethnoracial and gender minoritized youth often results in more harsh treatment by the legal system, which could influence recidivism measurement. Latent variable modeling of legal contact is understudied; thus, it is important to examine the feasibility of measuring this construct as a latent variable, including measurement invariance by gender.MethodsAmong 401 youth ages 12–18 years at first ever court contact, we examined three metrics of legal contact over a 2-year follow-up period: youth-report of arrest, caregiver-report of their adolescent’s arrest, and official records of the number of new court charges. We examined between-group differences on each metric based on gender and ethnoracial identity. We then measured: (1) the association between youths’ self-reported delinquency and each metric, (2) gender-specific associations between self-reported delinquency and each metric, and (3) gender-based measurement invariance for a latent recidivism variable using confirmatory factor analysis.ResultsYouth were consistent reporters of their own delinquent behavior and prospective legal contact measured by arrests. There were no between-group differences based on gender or ethnoracial identity for any legal contact measures. Delinquency and all legal contact variables were positively intercorrelated for the overall sample and the male subsample. For females, delinquency was not associated with caregiver-reported youth arrest or number of new charges. The latent legal contact variable had unique factor structures for male and female subsamples, suggesting no measurement invariance.DiscussionYouth-reported delinquency at first ever legal contact was most strongly associated with youth-reported arrest during a 2-year follow-up period, followed by caregiver-reported arrest, and the number of new charges. Unique latent variable factor structures for male and female subsamples suggests the inter-relation between legal contact variables is gender-specific. Stakeholders should consider prioritizing youth-reported delinquency since it is most strongly related to prospective youth-reported arrest.

Abstract Background Chiropractors’ clinical indicators for recommending preventive continued care to patients with low back pain include previous pain episodes, a history of long pain duration and improvement after initial treatment. Our objectives were, in a cohort of patients with neck pain, to examine whether these clinical indicators were associated with being recommended continued care beyond 4 weeks, and if so whether this recommendation was dependent of chiropractor characteristics, as well as if the number of clinical indicators influenced this recommendation. Methods In this multi-center observational study, 172 patients seeking care for a new episode of neck pain in chiropractic practice in Norway were included between September 2015 and May 2016. The chiropractors treated their patients as per usual, and for this study, baseline data and 4-week follow-up data were used. Patient data included the clinical indicators (1) previous episodes of neck pain, (2) a history of long duration neck pain and (3) improvement four weeks after initial treatment. The recruiting chiropractors were asked at 4-week follow-up if each patient was recommended continued care, defined as care planned beyond the first 4 weeks. Univariate and multivariable logistic regression models investigated the association between clinical indicators and the continued care recommendation, as well as the influence of chiropractor characteristics on this recommendation. Cross tabulations investigated the relationship between the number of indicators present and recommendation of continued care. Results Long duration of neck pain was the strongest clinical indicator for being recommended continued care 4 weeks after the initial treatment. Chiropractor characteristics were not associated with this recommendation. In patients with all three clinical indicators present, 39% were recommended continued care. When two and one indicators were present, the percentages of those recommended continued care were 25% and 10%, respectively. Conclusion Chiropractors recommended continued care for patients experiencing neck pain based on their history of long pain duration, and this was not influenced by characteristics of the chiropractor. This differs from previous studies of indicators for maintenance care in patients with low back pain.

Abstract Background Oil tanks containing a mixture of hydrocarbons, including sulphuric compounds, exploded and caught fire in an industrial harbour. This study assesses airway symptoms and lung function in the nearby population 1½ years after the explosion. Methods A cross-sectional study included individuals ≥18 years old. Individuals living 20 km away formed a control group. A questionnaire and spirometry tests were completed by 223 exposed individuals (response rate men 70%, women 75%) and 179 control individuals (response rate men 51%, women 65%). Regression analyses included adjustment for smoking, occupational exposure, atopy, infection in the preceding month and age. Analyses of symptoms were also adjusted for stress reactions related to the accident. Results Exposed individuals experienced significantly more blocked nose (odds ratio 1.7 [95% confidence interval 1.0, 2.8]), rhinorrhoea (1.6 [1.1, 3.3]), nose irritation (3.4 [2.0, 5.9]), sore throat (3.1 [1.8, 5.5]), morning cough (3.5 [2.0, 5.5]), daily cough (2.2 [1.4, 3.7]), cough >3 months a year (2.9 [1.5, 5.3]) and cough with phlegm (1.9 [1.2, 3.1]) than control individuals. A significantly increasing trend was found for nose symptoms and cough, depending on the proximity of home address to explosion site (daily cough, 3-6km 1.8 [1.0, 3.1], 1 adjusted mean difference −123 mL [95% confidence interval −232, -14]), FEV1% predicted −2.5 [−5.5, 0.5], FVC −173 mL [− 297, -50], FVC% predicted −3.1 [− 5.9, -0.4], and airway obstruction (GOLD II/III). Conclusions Based on cross sectional analyses, individuals living in an area with air pollution from an oil tank explosion had more airway symptoms and lower lung function than a control group 1½ years after the incident.

Data science and computational thinking (CT) skills are important STEM literacies necessary to make informed daily decisions. In elementary schools, particularly in rural areas, there is little instruction and limited research towards understanding and developing these literacies. Using a Research-Practice Partnership model (RPP; Coburn & Penuel, 2016) we conducted multimethod research investigating nine elementary teachers' perceptions of data science and related curriculum design during professional development (PD). Connected Learning theory, enhanced with Universal Design for Learning, guided ways we assisted teachers in designing the data science curriculum. Findings suggest teachers maintained high levels of interest in data science instruction and CT before and after the PD and increased their self-efficacy towards teaching data science. A thematic analysis revealed how a data science framework guided curriculum design and assisted teachers in defining, understanding, and co-creating the curriculum. During curriculum design, teachers shared the workload among partners, made collaborative design choices, integrated differentiation strategies, and felt confidence towards teaching data science. Identified challenges included locating data sets and the complexity of understanding data science and related software. This study addresses the research gap in data science education for elementary teachers and assists with successful strategies for data science PD and curricular design.

Data science is a highly interdisciplinary field that comprises various principles, methodologies, and guidelines for the analysis of data. The creation of appropriate curricula that use computational tools and teaching activities is necessary for building skills and knowledge in data science. However, much of the literature about data science curricula focuses on the undergraduate university level. In this study, we developed an introductory data science curriculum for an out of school enrichment program aimed at middle grade learners (ages 11-13). We observed how the participants in the program (n = 11) learned data science practices through the combination of nonprogramming activities and programming activities using the language R. The results revealed that participants in the program were able to investigate statistical questions of their creation, perform data analysis using statistics and the creation of data visuals, make meaning from their results, and communicate their findings. These results suggest that a series of learner-centered nonprogramming and programming activities using R can facilitate the learning of data science skills for middle-school age students.

Online spaces have the capacity to be powerful informal learning and identity development spaces for marginalized communities. However, there is still much work to be done to uncover these complex social identities using ethical big data analyses. In this study, I draw on the theory of Knowledgeable Agents of the Digital, data feminism, and critical reflexivity practices to engage with a #blackgirlmagic Twitter dataset from 2016 to 2019. Using Epistemic Network Analysis, findings suggest that the #blackgirlmagic community self-defined their social identities around Black beauty, academic/professional accomplishments, and social justice. Because the women and girls of #blackgirlmagic were agentive in rewriting and sharing narratives of themselves, they were acting as knowledgeable agents of the digital. These findings may be useful for (1) uncovering other instances of knowledgeable agents from non-dominant populations and how they navigate a racialized and gendered society, and (2) providing suggestions for analyzing online big data through an ethical, intersectional feminist lens.

Purpose: Is there a specific way of thinking about organisational learning in Nordic countries? Are concepts such as organisational learning and learning organisations imported, or do they emerge with specific meanings from more local discourses? Beyond that, are they supported by specific learning theories? The purpose of this paper is to trace the way that the concepts of organisational learning and learning organisations appear in research and policy documents in Norway and to identify what sort of learning theories pertain to those concepts. The authors discuss whether Norway's case exemplifies a Nordic way of thinking about learning in organisations. Design/methodology/approach: Through an archaeological investigation into the concepts of organisational learning and learning organisations, the authors explore the theoretical and cultural framing of the concepts in research and policy. The authors limit our work to large industrial field experiments conducted in the 1960s and to large education reform in the 2000s. Findings: During the industrial field experiments in the 1960s, the concept of organisational learning evolved to form participatory learning processes in non-hierarchical organisations able to contribute to democracy at work. Education policy in the 2000s, by contrast, imported the concept of the learning organisation that primarily viewed learning as an instrumental process of knowledge production. That strategy is incommensurable to what we define as a Nordic way, one in which learning is also understood as a cultural and social process advanced by democratic participation. Originality/value: The authors add to organisational learning theories by demonstrating the importance of cultural context for theories and showing that the understanding of learning is historically and culturally embedded.

Abstract Background After an explosion and fire in two tanks containing contaminated oil and sulphur products in a Norwegian industrial harbour in 2007, the surrounding area was polluted. This caused an intense smell, lasting until the waste was removed two years later. The present study reports examinations of tear film break up time among the population. The examinations were carried out because many of the people in the area complained of sore eyes. The purpose of the study was to assess the relationship between living or working close to the polluted area and tear film stability one and a half years after the explosion. Methods All persons working or living in an area less than six kilometres from the explosion site were invited to take part in the study together with a similar number of persons matched for age and gender living more than 20 kilometres away. Three groups were established: workers in the explosion area and inhabitants near the explosion area (but not working there) were considered to have been exposed, and inhabitants far away (who did not work in the explosion area) were considered to be unexposed. A total of 734 people were examined, and the response rate was 76 percent. Tear film stability was studied by assessing non-invasive break-up time (NIBUT) using ocular microscopy. In addition Self-reported Break Up Time (SBUT) was assessed by recording the time the subject could keep his or hers eyes open without blinking when watching a fixed point on a wall. Background information was obtained using a questionnaire. Non-parametric Wilcoxon-Mann-Whitney-tests with exact p-values and multiple logistic regression analyses were performed. Results Both NIBUT and SBUT were shorter among the male exposed workers than among the inhabitants both near and far away from the explosion area. This was also found for SBUT among males in a multiple logistic regression analysis, adjusting for age and smoking. Conclusions Reduced tear film stability was found among workers in an area where an explosion accident had occurred.

Abstract Background Around one-third of workdays lost in Norway are due to musculoskeletal conditions, with persistent (chronic) pain being the most frequent cause of sick leave and work disability. Increasing work participation for people with persistent pain improves their health, quality of life, and well-being and reduces poverty; however, it is not clear how to best help unemployed people who have persistent pain to return to work. The aim of this study is to examine if a matched work placement intervention featuring case manager support and work-focused healthcare improves return to work rates and quality of life for unemployed people in Norway with persistent pain who want to work. Methods We will use a cohort randomised controlled approach to test the effectiveness and cost-effectiveness of a matched work placement intervention featuring case manager support and work-focused healthcare compared to those receiving usual care in the cohort alone. We will recruit people aged 18–64, who have been out of work for at least 1 month, had pain for more than 3 months, and want to work. Initially, all (n = 228) will be recruited to an observational cohort study on the impact of being unemployed with persistent pain. We will then randomly select one in three to be offered the intervention. The primary outcome of sustained return to work will be measured using registry and self-reported data, while secondary outcomes include self-reported levels of health-related quality of life and physical and mental health. Outcomes will be measured at baseline and 3, 6, and 12 months post-randomisation. We will run a process evaluation parallel to the intervention exploring implementation, continuity of the intervention, reasons for participating, declining participation, and mechanisms behind cases of sustained return to work. An economic evaluation of the trial process will also be conducted. Discussion The ReISE intervention is designed to increase work participation for people with persistent pain. The intervention has the potential to improve work ability by collaboratively navigating obstacles to working. If successful, the intervention may be a viable option for helping people in this population. Trial registration ISRCTN Registry 85,437,524 Registered on 30 March 2022.

The objective was to explore the associations between patient involvement in the rehabilitation process and improvements in function and goal attainment in the first year after rehabilitation. The longitudinal multicenter study RehabNytte provided data from participants who had been referred to rehabilitation (n = 2113). Quality indicator (QI) pass rates (% yes) were used to assess patient involvement in the rehabilitation process. The Patient-Specific Functional Scale (PSFS) (10 = best possible) was used to assess function. The outcome QI on goal achievement (response options of yes/no) was used to assess goal attainment. Logistic regression and paired sample t-tests were used to examine associations and mean changes in function from rehabilitation admission up to 3, 6, and 12 months. Most participants (95%) were involved in goal-setting, which was positively associated with younger age (OR 0.97, 95% CI 0.95–0.99) and female sex (OR 1.87, 95% CI 1.15–3.02). Function improved over the follow-up period, with greater improvements in the active goal-setting group. Being involved in goal planning almost tripled the odds of goal attainment (OR 2.78, 95% CI 1.60–4.83) and involvement in the rehabilitation plan almost doubled it (OR 1.99, 95% CI 1.41–2.81). Most participants were involved in rehabilitation goal-setting/planning and being involved was associated with beneficial functional outcomes and greater goal attainment.

BackgroundWorkers in the salmon processing industry have an increased risk of developing respiratory diseases and other hypersensitivity responses due to occupational exposure to bioaerosols containing fish proteins and microorganisms, and related allergens. Little is known about effective measures to reduce bioaerosol exposure and about the extent of skin complaints among workers. In addition, while identification of risk factors is a core activity in disease prevention strategies, there is increasing interest in health-promoting factors, which is an understudied area in the salmon processing industry. ObjectiveThe overall aim of this ongoing study is to generate knowledge that can be used in tailored prevention of development or chronification of respiratory diseases, skin reactions, protein contact dermatitis, and allergy among salmon processing workers. The main objective is to identify effective methods to reduce bioaerosol exposure. Further objectives are to identify and characterize clinically relevant exposure agents, identify determinants of exposure, measure prevalence of work-related symptoms and disease, and identify health-promoting factors of the psychosocial work environment. MethodsData are collected during field studies in 9 salmon processing plants along the Norwegian coastline. Data collection comprises exposure measurements, health examinations, and questionnaires. A wide range of laboratory analyses will be used for further analysis and characterization of exposure agents. Suitable statistical analysis will be applied to the various outcomes of this comprehensive study. ResultsData collection started in September 2021 and was anticipated to be completed by March 2023, but was delayed due to the COVID-19 pandemic. Baseline data from all 9 plants included 673 participants for the health examinations and a total of 869 personal exposure measurements. A total of 740 workers answered the study’s main questionnaire on demographics, job characteristics, lifestyle, health, and health-promoting factors. Follow-up data collection is not completed yet. ConclusionsThis study will contribute to filling knowledge gaps concerning salmon workers’ work environment. This includes effective workplace measures for bioaerosol exposure reduction, increased knowledge on hypersensitivity, allergy, respiratory and dermal health, as well as health-promoting workplace factors. Together this will give a basis for improving the work environment, preventing occupational health-related diseases, and developing occupational exposure limits, which in turn will benefit employees, employers, occupational health services, researchers, clinicians, decision makers, and other stakeholders. Trial RegistrationClinicalTrials.gov NCT05039229; https://www.clinicaltrials.gov/study/NCT05039229 International Registered Report Identifier (IRRID)DERR1-10.2196/48790

Introduction Emphasis on public involvement (PI) in health research has increased in the last 20 years. However, there is limited literature on PI in planning and conducting population-based health research. This study aims to identify child and adolescent health research priorities among children and stakeholder groups in Northern Norway by inviting PI groups to collaborate with researchers to develop and conduct a research priority survey.Methods and analysis This is a community-based participatory research project. The methods for research prioritisation are informed by those developed by the James Lind Alliance. In addition, the survey design and engagement plans are developed in extensive collaboration with child and youth stakeholder groups. Nine PI groups have met three times to develop an anonymous child and youth health research priority survey, as well as strategies for recruitment and dissemination of results. All 5th–10th grade pupils in the Finnmark region will be invited to participate in the survey, as well as caretakers and adults working for and with children and youth. The survey results will be analysed in collaboration with the PI groups, and research priorities checked with existing research literature.Ethics and dissemination The study is registered and approved by the Data Protection Authorities at the Finnmark Hospital Trust and the Expert Committee for Sami Health Research. Descriptions of methods applied and the survey results will be published in popular and scientific publications.

Background Demand for organs exceeds the number of transplants available, underscoring the need to optimize organ donation procedures. However, protocols for determining brain death (BD)/death by neurological criteria (DNC) vary considerably worldwide. In Denmark, digital subtraction angiography (DSA) is the only legally approved confirmatory test for diagnosing BD/DNC. We investigated the effect of the time delay caused by (repeat) confirmatory DSA on the number of organs donated by patients meeting clinical criteria for BD/DNC. We hypothesized that, first, patients investigated with ≥2 DSAs donate fewer organs than those investigated with a single DSA; second, radiological interpretation of DSA is subject to interrater variability; and third, residual intracranial circulation is inversely correlated with inotropic blood pressure support. Methods All DSAs performed over a 7-year period as part of BD/DNC protocols at Rigshospitalet, Copenhagen University Hospital, Denmark, were included. Clinical data were extracted from electronic health records. DSAs were reinterpreted by an independent neurinterventionist blinded to the original radiological reports. Results We identified 130 DSAs in 100 eligible patients. Patients with ≥2 DSAs (n = 20) donated fewer organs (1.7 +/− 1.6 SD) than patients undergoing a single DSA (n = 80, 2.6 +/− 1.7 organs, p = 0.03), and they became less often donors (n = 12, 60%) than patients with just 1 DSA (n = 65, 81.3%; p = 0.04). Interrater agreement of radiological DSA interpretation was 88.5% (Cohen’s kappa = 0.76). Patients with self-maintained blood pressure had more often residual intracranial circulation (n = 13/26, 50%) than patients requiring inotropic support (n = 14/74, 18.9%; OR = 0.23, 95% CI [0.09–0.61]; p = 0.002). Discussion In potential donors who fulfill clinical BD/DNC criteria, delays caused by repetition of confirmatory DSA result in lost donors and organ transplants. Self-maintained blood pressure at the time of clinical BD/DNC increases the odds for residual intracranial circulation, creating diagnostic uncertainty because radiological DSA interpretation is not uniform. We suggest that avoiding unnecessary repetition of confirmatory investigations like DSA may result in more organs donated.

As big data algorithm usage becomes more ubiquitous, it will become critical for all young people, particularly those from historically marginalized populations, to have a deep understanding of data science that empowers them to enact change in their local communities and globally. In this study, we explore the concept of critical machine learning: integrating machine learning knowledge content with social, ethical, and political effects of algorithms. We modified an intergenerational participatory design approach known as cooperative inquiry to co-design a critical machine learning educational program with and for youth ages 9 - 13 in two after-school centers in the southern United States. Analyzing data from cognitive interviews, observations, and learner artifacts, we describe the roles of children and researchers as meta-design partners. Our findings suggest that cooperative inquiry and meta-design are suitable frameworks for designing critical machine learning educational environments that reflect children’s interests and values. This approach may increase youth engagement around the social, ethical, and political implications of large-scale machine learning algorithm deployment.

Virtual learning environments have the potential to support students' development of design skills in engineering education. However, few approaches exist for modeling and measuring design learning as it emerges in authentic practices, which often includes collaboration. This study merges learning sciences research with engineering design education to develop an approach for modeling and measuring design thinking. I propose a "connected design rationale" model which identifies relationships among design moves and rationale. Results from a qualitative examination of how professional engineers make connections among moves and rationales were used as the foundation to examine students in virtual internships. Using digital collaborative chat data and Epistemic Network Analysis (ENA), the discourse networks of students who had high and low scores in the virtual internship were compared to the discourse patterns of professional engineers to determine if measuring connected design rationale reveals meaningful differences between expert and novice design thinking. The results show a significant difference between high and low-performing students in terms of their patterns of connections and that high-performing students in the virtual internship made connections that were more like experts than low-performing students. Results suggest that a connected design rationale model distinguishes between experts and novices in meaningful ways and can be a robust approach for research in learning sciences and engineering education.

Abstract Background Health domains like pain, disability, and health-related quality of life are commonly used outcomes for musculoskeletal disorders. Most prognostic studies include only one outcome, and it is unknown if prognostic factors and models may be generic across different outcomes. The objectives of this study were to examine the correlation among commonly used outcomes for neck pain (pain intensity, disability, and health-related quality of life) and to explore how the predictive performance of a prognostic model differs across commonly used outcomes. Methods We conducted an observational prospective cohort study with data from patients with neck pain aged 18–84 years consulting Norwegian chiropractors. We used three different outcomes: pain intensity (Numeric Pain Rating Scale), the Neck Disability Index (NDI), and health-related quality of Life (EQ-5D). We assessed associations between change in outcome scores at 12-weeks follow-up with Pearson’s correlation coefficient. We used multivariable linear regression models to explore differences in explained variance and relationship between predictors and outcomes. Results The study sample included 1313 patients and 941 (72%) completed follow-up at 12 weeks. The strongest correlation was between NDI and EQ-5D (r = 0.57) while the weakest correlation was between EQ-5D and pain intensity (r = 0.39). The correlation between NDI and pain intensity was moderate (r = 0.53) In the final regression models, the explained variance ranged from adjusted R2 of 0.26 to 0.60, highest with NDI and lowest with pain intensity as outcome. The predictive contributions of the included predictors were similar across outcomes. Among the investigated predictors, pain patterns and the baseline measure of the corresponding outcome measure contributed the most to explained variance across all outcomes. Conclusions The highest correlation was found between NDI and EQ-5D and the lowest with pain intensity. The same prognostic model showed highest predictive performance with NDI as outcome and poorest with pain intensity as outcome. These results suggest that we need more knowledge on the reasons for the differences in predictive performance variation across outcomes.