Inclusive Community Aquatics Programming for Children with Developmental Challenges: A Community Participatory Action Research.

BACKGROUND: Children who are neurodiverse have traditionally been segregated from their peers in community-based programs, despite evidence of health benefits of inclusive education. OBJECTIVES: This community-initiated project aims to explore barriers and facilitators to inclusive aquatics programming for children with developmental and/or mental health challenges. METHODS: Using a participatory-action research methodology, semi-structured interviews and focus groups were conducted with 14 participants from various stakeholder groups, including parents of children who are neurodiverse, helping professionals, and community programmers. RESULTS: Participants described unique definitions of inclusion, from integration with neurotypical peers, to individualized goal-setting and achievement. Major facilitators include adequate resources, flexibility around accommodations, and motivated staff. Major barriers include social stigma, financial limitations, and lack of communication between caregivers and service providers. CONCLUSIONS: Participants felt strongly about the need to improve inclusion practices within aquatics-and other community-based-programs. Increased collaboration between families, community programmers, and helping professionals can foster better inclusion and outcomes for children who are neurodiverse. By incorporating various perspectives into the design of future programs, program administrators can ensure more equitable access such that all children are able to participate.

Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint.

The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.

Insights from teaching artificial intelligence to medical students in Canada.

Clinical artificial intelligence (AI) applications are rapidly developing but existing medical school curricula provide limited teaching covering this area. Here we describe an AI training curriculum we developed and delivered to Canadian medical undergraduates and provide recommendations for future training.

Health Care Students' Perspectives on Artificial Intelligence: Countrywide Survey in Canada.

BACKGROUND: Artificial intelligence (AI) is no longer a futuristic concept; it is increasingly being integrated into health care. As studies on attitudes toward AI have primarily focused on physicians, there is a need to assess the perspectives of students across health care disciplines to inform future curriculum development. OBJECTIVE: This study aims to explore and identify gaps in the knowledge that Canadian health care students have regarding AI, capture how health care students in different fields differ in their knowledge and perspectives on AI, and present student-identified ways that AI literacy may be incorporated into the health care curriculum. METHODS: The survey was developed from a narrative literature review of topics in attitudinal surveys on AI. The final survey comprised 15 items, including multiple-choice questions, pick-group-rank questions, 11-point Likert scale items, slider scale questions, and narrative questions. We used snowball and convenience sampling methods by distributing an email with a description and a link to the web-based survey to representatives from 18 Canadian schools. RESULTS: A total of 2167 students across 10 different health professions from 18 universities across Canada responded to the survey. Overall, 78.77% (1707/2167) predicted that AI technology would affect their careers within the coming decade and 74.5% (1595/2167) reported a positive outlook toward the emerging role of AI in their respective fields. Attitudes toward AI varied by discipline. Students, even those opposed to AI, identified the need to incorporate a basic understanding of AI into their curricula. CONCLUSIONS: We performed a nationwide survey of health care students across 10 different health professions in Canada. The findings would inform student-identified topics within AI and their preferred delivery formats, which would advance education across different health care professions.

Light Therapy for Patients With Bipolar Depression: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

OBJECTIVE: Bipolar disorder (BD) is challenging to treat, and fewer treatments are available for depressive episodes compared to mania. Light therapy is an evidence-based nonpharmacological treatment for seasonal and nonseasonal major depression, but fewer studies have examined its efficacy for patients with BD. Hence, we reviewed the evidence for adjunctive light therapy as a treatment for bipolar depression. METHODS: We conducted a systematic review of databases from inception to June 30, 2019, for randomized, double-blind, placebo-controlled trials of light therapy in patients with BD (CRD42019128996). The primary outcome was change in clinician-rated depressive symptom score; secondary outcomes included clinical response, remission, acceptability, and treatment-emergent mood switches. We quantitatively pooled outcomes using meta-analysis with random-effects models. RESULTS: We identified seven trials representing 259 patients with BD. Light therapy was associated with a significant improvement in Hamilton Depression Rating Scale score (standardized mean difference = 0.43, 95% confidence interval [CI], 0.04 to 0.82, P = 0.03). There was also a significant difference in favor of light therapy for clinical response (odds ratio [OR] = 2.32; 95% CI, 1.12 to 4.81; P = 0.024) but not for remission. There was no difference in affective switches between active light and control conditions (OR = 1.30; 95% CI, 0.38 to 4.44; P = 0.67). Study limitations included different light treatment parameters, small sample sizes, short treatment durations, and variable quality across trials. CONCLUSION: There is positive but nonconclusive evidence that adjunctive light therapy reduces symptoms of bipolar depression and increases clinical response. Light therapy is well tolerated with no increased risk of affective switch.