Introduction The lack of attention to transgender and gender diverse (TGD) people in undergraduate medical education (UME) is a point of concern, particularly among medical students. A project was undertaken to develop a UME curriculum framework for teaching the healthcare needs of TGD people. Methods Using a modified Delphi methodology, four rounds of surveys were presented to an expert stakeholder group that included content experts, generalist physicians, UME teaching faculty, and medical students. Questions covered what content should be taught, who should teach the content, and how much time should be dedicated for this teaching. Once the Delphi process was complete, feedback on the provisional framework was sought from members of the TGD community to ensure it represented their needs and perspectives. Results 71 panel members and 56 community members participated in the study. Core values included the scope of the framework, and topics such as inclusivity, and safety in practice and in teaching. The framework included terminology, epidemiology, medical and surgical treatment, mental health, sexual and reproductive health, and routine primary care. There was also guidance on who should teach, time to be allocated, and the learning environment. Discussion There is a clear need to train tomorrow’s doctors to provide competent and respectful healthcare services to and for TGD patients. Although local factors will likely shape the way in which this framework will be implemented in different contexts, this paper outlines a core UME-level curriculum framework for Canada and, potentially, for use in other parts of the world.

Abstract-The enhanced form of client-server, cluster and grid computing is termed as Cloud Computing. The cloud users can virtually access the resources over the internet. Task submitted by cloud users are responsible for efficiency and performance of cloud computing services. One of the most essential factors which increase the efficiency and performance of cloud environment by maximizing the resource utilization is termed as Task Scheduling. This paper deals with the survey of different scheduling algorithms used in cloud providers. Different scheduling algorithms are available to achieve the quality of service, performance and minimize execution time. Task scheduling is an essential downside within the cloud computing that has to be optimized by combining different parameter. This paper explains the comparison of several job scheduling techniques with respect to several parameters, like response time, load balance, execution time and makespan of job to find the best and efficient task scheduling algorithm under these parameters. The comparison of scheduling algorithms is also discussed in tabular form in this paper which helps in finding the best algorithms.

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Background Structural and interpersonal anti-Indigenous racism is prevalent in Canadian healthcare. The Truth and Reconciliation Commission calls on medical schools to address anti-Indigenous bias in students. We measured the prevalence of interpersonal anti-Indigenous bias among medical school applicants to understand how the medical school selection process selects for or against students with high levels of bias. Methods All applicants to a single university in the 2020–2021 admissions cycle were invited to participate. Explicit anti-Indigenous bias was measured using two sliding scale thermometers. The first asked how participants felt about Indigenous people (from 0, indicating ‘cold/unfavourable’ to 100, indicating ‘warm/favourable’) and the second asked whether participants preferred white (scored 100) or Indigenous people (scored 0). Participants then completed an implicit association test examining preferences for European or Indigenous faces (negative time latencies suggest preference for European faces). Explicit and implicit anti-Indigenous biases were compared by applicant demographics (including gender and racial identity), application status (offered an interview, offered admission, accepted a position), and compared to undergraduate medical and mathematics students. Results There were 595 applicant respondents (32.4% response rate, 64.2% cisgender women, 55.3% white). Applicants felt warmly toward Indigenous people (median 96 (IQR 80–100)), had no explicit preference for white or Indigenous people (median 50 (IQR 37–55), and had mild implicit preference for European faces (− 0.22 ms (IQR -0.54, 0.08 ms)). There were demographic differences associated with measures of explicit and implicit bias. Applicants who were offered admission had warmer feelings toward Indigenous people and greater preference for Indigenous people compared to those were not successful. Conclusions Medical school applicants did not have strong interpersonal explicit and implicit anti-Indigenous biases. Outlier participants with strong biases were not offered interviews or admission to medical school.

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