Individual attention is, at best, satisfactory and, at worst, self-destructive (Citton, 2017). Citton (2017) believes it is of great importance to reorient our attention to our priorities rather than divert them to the profit of others, and I would have to agree. My number one priority is my family, and every day, it is a struggle to ensure they receive my full attention. Dedicated attentiveness imparts dignity to the person receiving the attention (Citton, 2017), quite a powerful statement! This can be easier said than done in a world full of distractions, alerts and notifications. After reviewing the first pie chart, which breaks down the minutes of my day, it appears my attention is well distributed, primarily focusing on my family. I walked to the soccer field with my oldest daughter to watch my younger daughter play, we ate lunch and dinner together, picked out a Christmas tree and decorated our home. Despite the intention of being present and mindful of where my attention was going, I was still distracted by the digital technology in my life. Upon review of my screen time in phone settings, I learned that I had picked up my phone 153 times the same day and received 85 text messages and 28 other notifications from various applications. This constant demand for attention can increase stress, reduce critical thinking and disrupt focus (Citton, 2017).
Attentional activism is defined as the management of attention (McCreedy, 2024). Digital technologies depend on our attention in order to function (McCreedy, 2024), so how do we ensure that our attention, which is so valuable, is being utilized optimally? Citton (2017) suggests trying to understand what we can do with our attention rather than asking what we should be attentive to. Shifting towards an attention ecology where the value of something is in its use (McCreedy, 2021) would be an excellent place to start. In an attention ecology, attention is absorbed in one’s own learning rather than exchanged (McCreedy, 2024). The aspects of my day where I was most attentive included working on a puzzle, watching soccer and walking in nature. While working on the puzzle, I was most attentive primarily because the puzzle would not have been completed without my complete attention. While watching soccer, I was mindful of the value of my attention. Despite putting my phone on silent and out of sight, I had moments of distraction from the game where I had to stop, pause, and re-direct myself back to the game. This is, in part, because the game did not require my engagement to continue. Walking was the most challenging activity to ensure attentiveness. Paying attention to one’s own attention is a task that is potentially endless, walking without looking at my phone, and paying attention to my surroundings is something that I have been working on for the past year and will continue to have to work on due to the extraneous demands on my attention from work, school, parenting and social networks. Despite their many benefits, digital technologies amplify the fragmentations of our attention (Citton, 2017), so it is essential that we actively engage in meta-cognitive practices in order to impart dignity not only to others but also to ourselves.
References
Citton, Y. (2017). Introduction and conclusion: From attention economy to attention ecology. In Y. Citton, The ecology of attention. John Wiley & Sons.
McCready, S. (2024). Module 8: Attention [MOOC]. University of British Columbia.https://canvas.ubc.ca/courses/147974/pages/attention?module_item_id=7126888
A Critical Case Study: The Implementation of Hyflex at the College of New Caledonia
By Steph Takeda and Robyn Bernsen
ETEC 511: MET Program, Dr. Samuel McCready, University of British Columbia
IP 5: Global Health
The concept of health is complex, multifactorial and exists on many levels. In the above image, I have provided a visualization of the relationships that exist between an individual’s health and their environment. This environment extends beyond the physical body to potentially the furthest corner of the world. The Covid 19 pandemic not only illustrated how connected we are to the world on a global level and to each other, but it also highlighted and amplified the complexities and barriers that exist in our health care and educational system. As illustrated above, it was an accelerant, adding fuel to an existing flame. Although this global health crisis showcased the pre-existing inequities in education and potentially furthered them, it was also a mechanism for investigating our current teaching and learning structures and creating meaningful change (Boys, 2021).
What is global health and how does it connect to educational technology?
Global health focuses on improving health outcomes for all people worldwide and achieving health equity (Koplan et al., 2009). When faced with global health concerns, technology allows us to connect, creates access, and enhances collaboration despite the physical distance that separates us. We are all connected and can be affected by external factors that impact our health; however, the solutions to global health challenges often come from our community public health systems (Campbell et al., 2012). Closely linked to our public health systems and community well-being is that of our education system, which is, in turn, closely linked to technology in this rapidly evolving world. The creation of the COVID Education Alliance is evidence of how global health, education and technology impact each other. This alliance was created during the pandemic to address the challenges of digital transformation in the educational system due to the COVID-19 pandemic (COVID Education Alliance, 2020). Their goal was to address the shortcomings of the current education systems made evident by the COVID-19 pandemic and help systems adapt to the requirements of a rapidly changing world.
What lessons were learned from this most recent global health event?
There were many lessons learned during the Covid 19 pandemic specific to education. As a result of the immediate need to create physical distance to prevent viral spread, a rapid and extensive shift in pedagogy unfolded resulting in a near-total dependence on technology to sustain educational activities (McCready, 2024). This pivot to online learning revealed much about who can and cannot access education and how technology interacts with, and at times distances itself from, human experience (McCready, 2024). It was speculated that the pandemic would lead to unequal opportunities for learners with limited access to technology and parental support; however, yet research showed little evidence of this inequality when compared to pre-pandemic times (Kuhfled et al., 2020). Boys (2021) highlights how preexisting unconscious biases have always existed with face-to-face learning, and technology merely adds to this. The lesson highlighted here is that the pandemic emphasized inequities that have always existed but likely made these inequalities more visible.
Boy (2021) also discusses how the pandemic allowed for a deeper understanding of the gaps between what the teacher assumes and what the learner understands. His article discusses the perception that in-person learning is preferable and the underlying assumptions that can exist within this learning environment. For example, the student who shows up to class prepared is often coined the ideal learner as it is assumed this learner possesses preferred character traits (Boys, 2021). This bias has trickled into virtual learning, with assumptions being made about learners with their cameras off, for example. The lesson or, more accurately, the question posed, is does face-to-face interaction needs to exist to allow for effective learning?
The pandemic also allowed academics to connect online in various ways and share ideas on how to improve curriculum, resources, teaching methods, and student experiences (Boys, 2021). The lesson? We all have a lot to learn and can learn from each other.
The final lesson I will highlight is that to prepare learners for the future, major changes are needed in what we learn, how we learn, and how we teach (COVID Education Alliance, 2020). The COVID Education Alliance (2020) recognized how technology can facilitate access to online educational content, resulting in richer educational experiences both online and in schools. By leveraging educational technology tools, we can build knowledge, character, judgment, resilience and social awareness (COVID Education Alliance, 2020).
References
Boys, J. (2021). Exploring inequalities in the social, spatial and material practices of teaching and learning in pandemic times. Postdigital Science and Education, 4(1), 13-32.
Campbell, R.M., Pleic, M., & Connolly, H. (2012, June 2). The importance of a common global health definition: How Canada’s definition influences its strategic direction in global health.Links to an external site. Journal of Global Health, 2(1).
COVID Education Alliance. (2020). COVID Education Alliance (COVIDEA): Adapting education systems to a fast changing and increasingly digital world through the use of appropriate technologies. Foundation for Global Governance and Sustainability (FOGGS) and Platform for Transformative Technologies. https://www.foggs.org/covidea/
Koplan, J. P., Bond, T. C., Merson, M. H., Reddy, K. S., Rodriguez, M. H., Sewankambo, N. K., Wasserheit, J. N., & Consortium of Universities for Global Health Executive Board. (2009). Towards a common definition of global health. The Lancet, 373(9679), 1993–1995.
Kuhfeld, M., Soland, J., Tarasawa, B., Johnson, A., Ruzek, E., & Lewis, K. (2020, December 3). How is COVID-19 affecting student learning? Question 2. Brown Center Chalkboard, Brookings.
McCready, S. (2024). Module 5: Global health [MOOC]. Univeristy of British Columbia. https://canvas.ubc.ca/courses147974/pages/.global-health?module_item_id=7126882
Posted in IPs
| Alan Mathison Turing (1912-1954) | |
| In my own words
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Turing received his doctorate from Princeton University in 1938 and worked for the Government Code and Cypher School at Britain’s codebreaking center (“Alan Mathison,” 2024). Turing devised techniques to break German ciphers, which are algorithms used to perform encryption/decryption (“Alan Mathison,” 2024). Turing was also responsible for the design of the automatic computer engine and the Manchester computer, both stored-program computers (“Alan Mathison,” 2024).
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| Chat GPT | Alan Turing laid the foundation for artificial intelligence with his concept of the Turing Machine, a model for computation. His 1950 paper introduced the “Turing Test,” proposing that a machine could be considered intelligent if it could imitate human responses. Turing’s work established key principles in AI and computer science (OpenAI, 2024).
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| John McCarthy (1927-2011) | |
| In my own words
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John McCarthy was both a computer scientist as well a cognitive scientist and is considered one of the “founding fathers” of artificial intelligence (“John McCarthy,” 2024). In the summer of 1956, he attended the Dartmouth conference and together with Marvin Minsky, Nathanial Rochester, and Claude E. Shannon, he coauthored a proposal which coined the term “artificial intelligence” (“John McCarthy,” 2024). This conference was thought to be the beginning of artificial intelligence as a field of study (“John McCarthy,” 2024).
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| Chat GPT | “John McCarthy, a pioneer in artificial intelligence (AI), coined the term “artificial intelligence” in 1956. He developed the programming language Lisp, which became fundamental to AI research. McCarthy also contributed to time-sharing systems and formalized concepts like reasoning and knowledge representation, advancing AI’s theoretical foundations” (OpenAI, 2024).
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| Herb Simon (1916-2001) | |
| In my own words
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Simon, in collaboration with Allen Newell, created the Logic Theory Machine, a computer program engineered to perform automated reasoning. He was considered a pioneer of artificial intelligence (“Herbert A. Simon,” 2024). He also collaborated on the General Problem Solver, a machine created to work as a universal problem solver (“Herbert A. Simon,” 2024).
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| Chat GPT | Herb Simon was a key figure in artificial intelligence, co-developing the Logic Theory Machine and General Problem Solver, early AI programs. He explored human decision-making and problem-solving, proposing the theory of bounded rationality. Simon’s interdisciplinary work in AI, psychology, and economics significantly shaped cognitive science and AI research (OpenAI, 2024).
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| Marvin Minsky (1927-2016) | |
| In my own words
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Minsky developed the Society of Mind theory while at the Massachusetts Institute of Technology (MIT) Artificial Intelligence Lab (“Marvin Minsky,” 2024). This theory seeks to explain intelligence as a result of the interactions between “non-intelligent” parts (“Marvin Minsky,” 2024). Minsky visualized the brain as a machine and felt that its functions could be replicated by a computer (BBC, 2016).
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| Chat GPT | Marvin Minsky, a founding figure in artificial intelligence, co-founded the MIT AI Lab and advanced the field with key ideas on machine learning, perception, and robotics. He developed the “Society of Mind” theory, explaining intelligence as a collection of simpler processes, influencing AI’s approach to understanding human cognition (OpenAI, 2024).
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| Timnit Gebru (1982) | |
| In my own words
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Timnit Gebru has worked for Apple, Microsoft, and, more recently, Google, where she co-led a team on the ethics of artificial intelligence (“Timnit Gebru,” 2024). Her employment at Google came to an abrupt end after she co-authored the paper titled “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” This paper discussed the risks of large language models from an environmental, financial and prejudicial lens (“Timnit Gebru,” 2024).
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| Chat GPT | Timnit Gebru is a leading AI researcher known for her work on algorithmic bias and ethical AI. She co-authored influential studies on the racial and gender biases in facial recognition systems and co-founded Black in AI, advocating for diversity and accountability in AI research and its societal impacts (OpenAI, 2024).
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| In my own words
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One of the critical differences between programming language and human language is that programming language does not change (Harris, 2018). Unlike human language, programme language cannot evolve and develop over time, and its meaning does not alter depending on the context (Harris, 2018). Take the slang terms currently trending with Generation Alpha, for example. The word “Ohio,” which indicates a US state for most adults, now indicates someone is bad, weird or cringy when placed in the context of an elementary school student. The meaning of this word evolved due to a 2016 Tumbler post (Mitchell, 2024). Human language is also both logical and emotional. If spoken, it involves body language, intonation, volume, and many other nonverbal clues (Harris, 2018). This interpretation of how something is said does not exist with programming language.
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| Chat GPT | Machine (programming) languages and human (natural) languages differ fundamentally in purpose, structure, and interpretation. Machine languages, such as Python or C++, are designed to communicate instructions to computers with precise syntax and logic, requiring exact commands for specific tasks. They are unambiguous, with a strict set of rules that computers directly interpret or compile into binary code (OpenAI, 2024).
In contrast, human languages, like English or Spanish, are naturally evolved for communication between people. They are flexible, context-dependent, and often ambiguous, allowing for nuance, creativity, and interpretation. Humans use them to express complex ideas, emotions, and cultural concepts, unlike the rigid structure of programming languages (OpenAI, 2024).
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| In my own words
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In order to explain how machine intelligence differs from human intelligence, intelligence must first be defined. Chollet (2019) argues that solely measuring skill falls short of measuring intelligence. Although it requires intelligence to perform a specialized task, the ability to adapt and apply knowledge in different contexts requires a higher level of intelligence. Cholet (2019) explains how AI researchers must shift from narrow tasks to evaluating flexibility, robustness and generalization to emulate human intelligence. This can be further supported by Bloom’s Taxonomy, which illustrates a hierarchy of cognitive domains (University of Waterloo, 2024). Higher levels of thinking include analysis, synthesis and evaluation, which require the ability to think critically and problem-solve (University of Waterloo, 2024), something a machine cannot do… yet. |
| Chat GPT | Machine (artificial) intelligence and human intelligence differ significantly in their nature and capabilities. Machine intelligence, driven by algorithms and data, excels in processing vast amounts of information quickly and performing specific tasks, such as calculations or pattern recognition, often surpassing human efficiency in these areas. However, it lacks genuine understanding, emotions, and consciousness (OpenAI, 2024).
In contrast, human intelligence is characterized by complex cognitive abilities, including emotional reasoning, creativity, and moral judgment. Humans can adapt to novel situations, think abstractly, and draw on personal experiences, while AI operates within predefined parameters. Ultimately, human intelligence is more holistic and contextually aware compared to the specialized functions of AI (OpenAI, 2024). |
| In my own words
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Machine learning works on old data (Heilweil, 2020). In addition, it is often difficult to know, as a consumer, what data contributed to the “machine learning” and the degree of accuracy, bias, or prejudice within the data set (Heilweil, 2020). Although humans often refer to “old data” to learn, they have the ability to incorporate new information and analyze its relevance. Depending on the level of scrutiny and critical thinking, a human has the capacity to determine the source of data, the level of controls used, and, to a certain extent, the degree of bias impacting the data from which they are learning.
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| Chat GPT | Machine learning and human learning differ fundamentally in processes and capabilities. Machine learning relies on algorithms that analyze vast datasets to identify patterns and make predictions, often requiring large amounts of labeled data and extensive computational power. It learns through iterations and can improve performance over time but lacks understanding or context (OpenAI, 2024).
In contrast, human learning is a complex, experiential process influenced by emotions, social interactions, and cognitive development. Humans can generalize knowledge from fewer examples, apply critical thinking, and adapt to novel situations. Moreover, human learning incorporates creativity, intuition, and the ability to reflect on experiences, enabling deeper understanding and insight (OpenAI, 2024).
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| When I started this assignment, I felt confident. I started with questions 2 through 4. I read the articles, took notes, and contemplated my opinion on the posed questions. I felt certain that the intellect of a human being exists on a higher playing field than that of a machine. The answers generated by ChatGPT for questions 2, 3, and 4 were more of a definition as opposed to my analysis and reflection on the human versus the machine. For example, each answer was scripted and presented in a similar format, beginning with a statement such as “machine learning and human learning differ” followed by “in contrast” (OpenAI, 2024). However, when I returned to question 1, the “intelligence” of the machine became evident. I could have easily spent an entire day researching the five individuals discussed in question 1. In less than 5 seconds, chat GPT was able to scour the internet looking for information and content relevant to each individual’s role in artificial intelligence. In addition to that, I could request the “machine” to summarize this content in approximately 50 words. Of course, the legitimacy and accuracy of ChatGPT’s content is undeterminable. Unlike my carefully referenced text, ChatGPT’s paragraphs provide no clear path to where the content came from, and with that, my confidence has been restored… at least for now.
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References
Alan Turing (2024, September 24). Wikipedia. https://en.wikipedia.org/wiki/Alan_Turing
BBC News. (2016, January 26). AI pioneer Marvin Minsky dies aged 88.
Chollet, F. (2019, November 5). On the measure of intelligence
Heilweil, R. (2020, February 18). Why algorithms can be racist and sexist. A computer can make a decision faster. That doesn’t make it fair.Links to an external site. Vox.
Herbert A. Simon (2024, September 13) Wikipedia. https://en.wikipedia.org/wiki/Herbert_A._Simon
John McCarthy (computer scientist). (2024, July 10). Wikipedia. https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist)
Harris, A. (2018, October 31). Languages vs. programming languages.Links to an external site. Medium.
Mitchell, A. (2024, August 23). ‘Ohio’ is the latest Gen Alpha and Gen Z slang word – and they’re not using it in a nice way: ‘Sort of weird, absurd.’ The New York Post. https://nypost.com/2024/08/23/lifestyle/ohio-is-gen-z-and-gen-alpha-slang-for-weird-or-cringe/
OpenAI. (2024). ChatGPT (Sept 24 version) [Large language model]. https://chat.openai.com/chat
Timnit Gebru (2024, August 26). Wikipedia. https://en.wikipedia.org/wiki/Timnit_Gebru
University of Waterloo. (2024). Bloom’s taxonomy. https://uwaterloo.ca/centre-for-teaching-excellence/catalogs/tip-sheets/blooms-taxonomy#:~:text=Bloom’s%20Taxonomy%20comprises%20three%20learning,of%20the%20Taxonomy%20are%20hierarchical.
Posted in IPs
IP 1: User, Uses and Usability
The Concept of Usability
When reflecting on the concept of usability, as described by Issa and Isaiais (2015), I struggled to capture the complexity and connectivity of the key concepts in a short paragraph. Instead, I opted to demonstrate my understanding and vision using the image of an onion with multiple layers.
Alternatively, to Issa and Isaiais’ (2015) Venn diagram, I visualize the concept of usability with the user at the core. Each layer interacts with the layers beyond and beneath and impacts the overall outcome of the user’s experience. Additionally, the user’s experience is influenced by more than their personal attributes. They are impacted by what they are trying to accomplish, the system’s design, and perhaps most importantly, by the external factors that impact their lives.
Educational Perspective
Additional considerations must be addressed when looking at usability from an educational perspective. Bryson et al.’s (2015) description of “low floors,” “high ceilings,” and “wide walls” resonated with me and can be incorporated into the above diagram. In education, the center of the onion described above needs to have flexible and not rigid borders to ensure that all users can fit within it. The layers beyond the core impact each user differently, and the capacity of the user will vary because of that.
Configuring the User
In his 1990 paper, Configuring the User: The Case of Usability Trials, Woolgar discusses an 18-month ethnographic study he conducted at a successful, medium-sized company that manufactures microcomputers and other products for educational purposes. For this study, Woolgar joined their project management team to launch a new range of microcomputers and followed this project from inception to launch. Throughout this paper, he uses the metaphor of the “machine as text” to describe the configuration of the user and explores how “machines,” or computers, are constructed and used.
Woolgar (1990) describes how the user’s decisions and actions were impacted throughout the machine’s construction with the presence of “objective observers” who would intervene with the user when the subject was thought to be going off track. For example, when user Ruth attempted to connect the printer with an incorrect lead and was unable to do so, she questioned herself, and the observers intervened. It then becomes clear that there was a problem with the lead, shifting the blame from the user to the machine and highlighting the oscillation between these entities. “A whole series of contingencies arose which demanded their frequent intervention,” thus disturbing the user-machine relationship.
The observers also made several assumptions, further disrupting this relationship: “Let’s assume you succeeded there, which I think you did” (Woolgar, 1990, p. 85), “You’d know Windows was on there, yah, I think you would know, wouldn’t you?” (Woolgar, 1990, p.86). These assumptions impacted the user, their relationship with the machine and thus the trial’s outcome.
Compare and Contrast
“…the usability evaluation stage is an effective method by which a software development team can establish the positive and negative aspects of its prototype releases, and make the required changes before the system is delivered to the target users” (Issa & Isaias, 2015, p. 29). “
…the design and production of a new entity…amounts to a process of configuring its user, where ‘configuring’ includes defining the identity of putative users, and setting constraints upon their likely future actions” (Woolgar, 1990).
Woolgar (1990) explains the complexity of entities and the relationships between them. “The machine can only be understood in terms of its relationship with other entities of its phenomenal world” (p.67). When a new entity is introduced, it disrupts and potentially changes the moral order (Woolgar, 1990). He implies that introducing a new interface results in the user’s configuration and that there is a fluid relationship between the two, both influencing one another. Issa and Isaiais (2015) discuss the importance of the evaluation stage in ensuring optimal human-computer interaction. This involves a thoughtful process of design, implementation, and evaluation, as well as an awareness that organizational, social, and psychological factors will influence this.
Although I appreciate how Woolgar’s 1990 ethnographic study on configuring the user forced me to examine the relationship between the machine and the user, how the user was evaluated leaves me with some skepticism. The machine likely influenced the users in Woolgar’s study on some level; however, I suspect the flawed evaluative process and influential observers had a more significant influence. With a background in science and healthcare, I lean more towards Issa and Isaiais’ (2015) emphasis on the evaluative process and believe this can be objective and unbiased if thoughtfully and scientifically implemented. With that said, it is clear to me that the entity of the machine can significantly impact how we view ourselves and how we see and interact with the world around us; thus, it is impossible to deny that the machine and the user do not mutually shape each other on some level.
References
Bryson, M., Bereiter, C., Scardamalia, M., & Joram, E. (1991). Going beyond the problem as given: Problem-solving in expert and novice writers. In R. J. Sternberg & P. A. Frensch (Eds.), Complex problem solving: Principles and mechanisms (pp. 61–84). Lawrence Erlbaum Associates, Inc.
Issa, T., & Isaias, P. (2015). Usability and human-computer interaction (HCI). In Sustainable Design (pp. 19-35). Springer.
Woolgar, S. (1990). Configuring the user: The case of usability trials. The Sociological Review, 38(1, Suppl.), S58-S99.
Cultural humility is a lifelong process of self-reflection and self-critique (BCCNM, 2024). This Truth and Reconciliation assignment has allowed me to continue my journey toward cultural humility through this self-reflective practice and knowledge acquisition which I am grateful for.
When tasked to identify a significant body of text representing how Indigeneity and Indigenous people are represented in texts, several works came to mind. I reflected on fictional stories I was exposed to during my youth, such as the novel Indian in the Cupboard or the Disney movie Pocahontas, and how they contributed to the anti-indigenous socialization that existed throughout my youth. I also reflected on the In Plain Sight report released in November 2020 and how this document has impacted my responsibility toward truth and reconciliation as a healthcare professional. The In Plain Sight report found evidence of indigenous-specific racism in BC and attributes this racism to a lower life expectancy, increased infant mortality and the increased presence of chronic health conditions in our Indigenous population. With this in mind, I wanted to better understand and answer the following question: What level of implicit and explicit bias exists in medical journals? I conducted a journal review using the terms Indian, Indigenous, Aboriginal, First Nations, and Native to gain awareness of the language used in medical journals. I wanted to look at the most historic journals possible and organized my search findings to list from oldest to newest. I was unable to access many of the journals electronically. However, the titles alone allude to the explicit bias and racism that has occurred in healthcare historically. For example, “We, too, have an Indian Problem,” published in 1951 by O’Hara, or the journal article “A Nurse among the Indians” by Grondin, whose title suggests “othering” language. More recent articles, however, were accessible. An article by Thompson (1990) discussed the prevalence of heavy birth weights in Native Indians in British Columbia. The lack of people first language was evident throughout the body of this text, for example, “the Natives were economically poor” (p. 443). There was also evidence of assumptions and bias: “Although no data on height or weight of British Columbian Native women were located, it is generally believed that Natives are heavier than non-Natives” (p. 446). I found the term Native Indians was commonly used in the 1990s and therefore added this to my search.
After sifting through multiple research articles, I found myself asking a different question. The In Plain Sight report came out in 2020, but did awareness surrounding the lack of Indigenous cultural safety exist in nursing research prior to this conversation? When did our language change from Indian/Native Indian to more appropriate and people-first terminology in medicine? I added the search terms of cultural safety, colonialism and anti-racism to gain insight into any progress made over the past few decades. Trying to narrow down a topic as expansive as racism in healthcare was challenging; however, limiting my search to Canada and specifying Indigenous racism allowed me to streamline the research. I was pleased to find evidence to support that this work was being done. My search resulted in articles discussing colonialism’s impact on Aboriginal health and health equity from the early 2000s. For example, Brown et al.’s article “The Relevance of Postcolonial Theoretical Perspectives to Research in Aboriginal Health,” published in 2005 and “Our Land, Our Language: Connecting Dispossession and Health Equity in an Indigenous Context” (Brown, 2012). Even more promising was the increasing amount of research conducted in the past 10 years.
This assignment aimed to understand historical teachings to pave the way toward reconciliation. I hope that my post has also highlighted how anti-indigenous socialization has existed not only in our educational system but also in our healthcare system and the work that needs to be done to ensure Indigenous cultural safety.
References
British Columbia College of Nurses & Midwives. (2024). Indigenous Cultural Safety, Cultural Humility, and Anti-Racism. https://www.bccnm.ca/RN/PracticeStandards/Pages/CulturalSafetyHumility.aspx
Browne, A.J., Smye, V.L. & Varcoe, C. (2005). The relevance of postcolonial theoretical perspectives to research in Aboriginal health. Canadian Journal of Nursing Research, 37(4), 16-37.
Brown H.J., McPherson G., Peterson R., Newman V. & Cranmer B. (2012). Our land, our language: Connecting dispossession and health equity in an indigenous context. Canadian Journal of Nursing Research, 44(2), 44-63. http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=med9&NEWS=N&AN=22894006.
Grondin, O. (1954). A nurse among the Indians. Canadian Nurse, 50(9), 730-2. http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=med1&NEWS=N&AN=13190501.
O’Hara, H. (1951). We, too, have an Indian problem. Nursing World, 125, 280-282.
Thomson, M. (1990). Heavy Birthweight in Native Indians of British Columbia. Canadian Journal of Public Health / Revue Canadienne de Sante’e Publique, 81(6), 443–446. http://www.jstor.org/stable/41989958
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