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BACKGROUND: Situated within a larger project entitled "Exploring the Need for a Uniquely Different Approach in Northern Ontario: A Study of Socially Accountable Artificial Intelligence," this rapid review provides a broad look into how social accountability as an equity-oriented health policy strategy is guiding artificial intelligence (AI) across the Canadian health care landscape, particularly for marginalized regions and populations. This review synthesizes existing literature to answer the question: How is AI present and impacted by social accountability across the health care landscape in Canada? METHODOLOGY: A multidisciplinary expert panel with experience in diverse health care roles and computer sciences was assembled from multiple institutions in Northern Ontario to guide the study design and research team. A search strategy was developed that broadly reflected the concepts of social accountability, AI and health care in Canada. EMBASE and Medline databases were searched for articles, which were reviewed for inclusion by 2 independent reviewers. Search results, a description of the studies, and a thematic analysis of the included studies were reported as the primary outcome. PRINCIPAL FINDINGS: The search strategy yielded 679 articles of which 36 relevant studies were included. There were no studies identified that were guided by a comprehensive, equity-oriented social accountability strategy. Three major themes emerged from the thematic analysis: (1) designing equity into AI; (2) policies and regulations for AI; and (3) the inclusion of community voices in the implementation of AI in health care. Across the 3 main themes, equity, marginalized populations, and the need for community and partner engagement were frequently referenced, which are key concepts of a social accountability strategy. CONCLUSION: The findings suggest that unless there is a course correction, AI in the Canadian health care landscape will worsen the digital divide and health inequity. Social accountability as an equity-oriented strategy for AI could catalyze many of the changes required to prevent a worsening of the digital divide caused by the AI revolution in health care in Canada and should raise concerns for other global contexts.

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BACKGROUND: Mining is a hazardous occupation with elevated rates of lost-time injury and disability. OBJECTIVE: The purpose of this study is twofold: 1) To identify the type of lost-time injuries in the mining workforce, regardless of the kind of mining and 2) To examine the antecedent factors to the occupational injury (lost-time injuries). METHODS: We identified and extracted primary papers related to lost-time injuries in the mining sector by conducting a systematic search of the electronic literature in the eight health and related databases. RESULTS: We critically reviewed nine articles in the mining sector that examined lost-time injuries. Musculoskeletal injuries (hand, back, limbs, fractures, lacerations and muscle contusions), slips and falls were identified as types of lost-time injuries. The review identified the following antecedent factors related to lost-time injuries: the mining work environment (underground mining), being male, age, working with mining equipment, organizational size, falling objects, disease status, job training and lack of occupational safety management teams, recovery time, social supports, access to health services, pre-injury health status and susceptibility to injury. DISCUSSIONS: The mining sector is a hazardous environment that increases workers' susceptibility to occupational injuries. There is a need to create and implement monitoring systems of lost-time injuries to implement prevention programs.

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We describe the protocol through which we identify and characterize dynein subunit genes in the ciliated protozoan Tetrahymena thermophila. The gene(s) of interest is found by searching the Tetrahymena genome, and it is characterized in silico including the prediction of the open reading frame and identification of likely introns. The gene is then characterized experimentally, including the confirmation of the exon-intron organization of the gene and the measurement of the expression of the gene in nondeciliated and reciliating cells. In order to understand the function of the gene product, the gene is modified-for example, deleted, overexpressed, or epitope-tagged-using the straightforward gene replacement strategies available with Tetrahymena. The effect(s) of the dynein gene modification is evaluated by examining transformants for ciliary traits including cell motility, ciliogenesis, cell division, and the engulfment of particles through the oral apparatus. The multistepped protocol enables undergraduate students to engage in short- and long-term experiments. In our laboratory during the last 6 years, more than two dozen undergraduate students have used these methods to investigate dynein subunit genes.

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