<i>Black History Month 2022—Celebrating the Contributions of Black Nurses in Health Care</i>
Bibliographic record
Abstract
Black History Month 2022—Celebrating Contributions of Black Nurses in Health Care is part of a virtual and curated display that encourages the participant to learn about Black nurses in history, to take a closer look at the significant contributions of Black nurses in Canada, and to celebrate Black nurses across the world who have paved the way to better health care. Co-created by Dr. Lydia Wytenbroek, doctoral student Ismalia De Sousa, and five BSN students, and with the support of the Consortium for Nursing History Inquiry in the UBC-V, the purpose of this flipbook is to share knowledge in a manner that engages the viewer and motivates them to work toward ending racism and other forms of oppression within health and educational systems. The flipbook encourages self-reflection on how historical events and the professionalization of nursing have contributed to current health and social inequities through an easy-to-read, non-biased, visual format. The photographs and copy tell a story of immigration, public health, social reform, and progress in professional nursing.The flipbook is organized in a way that tells the story of ten Black nurses from Canada and other parts of the globe between 1805 and the late 1960s. Interspersed between photos and content related to the background of each nurse are slides with questions that encourage the viewer to consider various ideas such as how racism, segregation, and discrimination affected the nursing profession. Following each question, the viewer will find answers to the question that are intended to stimulate further thought and discussion. Some of the nurses in the flipbook will be familiar to those who have knowledge of nursing history. Others will be less well known. A unifying theme for many of the nurses is that each was known as the first Black nurse to complete various professional milestones. Examples include Mary Mahoney, the first Black registered nurse in the United States, and Cecilia Antyi Makiwane, the first Black South African to graduate from nursing school in 1908. Others are less well known, yet each nurse made a significant contribution to the profession and to the improvement of health care outcomes in their countries.Interspersed throughout the flipbook are archival photos that are visually interesting and provide data to support the stories of each nurse. Documentation of archival newspaper articles, transcripts, birth records, and census data are found throughout. One of the most interesting slides in the flipbook contains census data and a report by Jessie Sleet Scales, a Black Canadian nurse who practiced nursing in the United States. In the report Scales provides a comprehensive list of her duties as a district nurse in New York City. She notes that during October and November, she visited forty-six Black families, and made 156 house calls for illnesses that included consumption, chicken pox, cancer, and diphtheria. She also applied poultices, provided dressing changes, washed and dressed newborn babies, and cared for mothers. The addition of archival data throughout strengthens the overall narrative and provides a depth of understanding that increases viewer interest and engagement.The overarching theme of the flipbook is that racism is a structural determinant of health. This idea is in part attributed by the authors to noted educator and civil rights activist, W. E. B. DuBois, who identified, as early as 1906, the idea that multifactorial social, political, and economic structures explain health inequities. The flipbook examines various examples of racism in Canada as well as other countries to provide a compelling argument supporting these ideas. A comprehensive bibliography is found at the end of the flipbook with links to other websites that encourage the viewer to delve deeper into the subject.The flipbook is easy to navigate, visually interesting, and engaging to the viewer. The questions asked throughout stimulate additional questions and encourage the reader to consider how they can get involved and personally work to eliminate racism in health care. Guiding questions at the end encourage the viewer to reflect, for example, on why Florence Nightingale is considered the pioneer of modern nursing in spite of the examples of Black and indigenous nurses who clearly contributed significantly to professional progress. It is a timely, relevant, easy-to-read resource that students, novice historians, and anyone interested in nursing history will enjoy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.131 | 0.025 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".