Unlocking the Power of Allyship: Giving Health Care Workers the Tools to Take Action Against Inequities and Racism - Noone, Damien; Robinson, Lisa A.; Niles, Chavon; Narang, Indra (NEJM Catalyst Innovations in Care Delivery, 2022)
Everyone, not just marginalized groups, must fight racism and oppression. Members of privileged groups must learn to be allies. Toronto’s Hospital for Sick Children developed a series of workshops to teach them how.Summary Oppression and racism are pervasive in the health care environment and directly impact patients, physicians, and other health care practitioners. Fostering and promoting allyship skills can increase inclusion in health care and advance systemic change. The Hospital for Sick Children in Toronto developed a foundational allyship workshop to increase awareness and understanding of interpersonal, systemic, and institutional oppression and discrimination in the large pediatric academic center. The workshop emphasizes individual accountability and how to speak up and act against oppression through critical allyship. The authors report promising results around the engagement and desire to practice allyship amongst health care providers.
Keywords: allié·e·s; amériques; approches et analyses; canada; cours; discipline; médecine; ontario; oppression; premier cycle; pédagogie; pédiatrie; racisme; régions géographiques; résistance; santé; sciences; sex2283; systèmes d'oppressions; thématiques; toronto; violences

Data feminism - D'Ignazio, Catherine; Klein, Lauren F. (ideas series, 2020)
"A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics--one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever 'speak for themselves.' Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed."-
Keywords: approches et analyses; discipline; données scientifiques; histoire et politique; intersectionnalité; patriarcat; privilèges; projet charge mentale; pédagogie; racisme; rapports de pouvoir; ressource en ligne; sciences; sciences humaines; systèmes d'oppressions; thématiques; travail; travail scientifique; épistémologie