Disrupting Racism in Ontario Midwifery
Bibliographic record
Abstract
INTRODUCTION: There are a limited number of Canadian studies that explore the experiences of racism among health care providers who are Black, Indigenous, or people of color (BIPOC), and specifically within the context of midwifery in Ontario. More information is needed to better understand how to achieve racial equity and justice at all levels of the midwifery profession. METHODS: Semistructured key informant interviews were conducted with racialized midwives in Ontario to understand how racism manifests in the midwifery profession and to conduct a needs assessment of interventions required. The researchers used thematic analysis to identify patterns and themes within the data and to develop a better understanding of participants' experiences and perspectives. RESULTS: Ten racialized midwives participated in key informant interviews. The vast majority of participants reported experiences of racism in their work as a midwife, including being subject to or witnessing racism from clients and colleagues, tokenism, and exclusionary hiring practices. More than half of participants also emphasized their commitment to providing culturally concordant care for BIPOC clients. Participants relayed that access to BIPOC-centered gatherings, workshops, peer reviews, conferences, support groups, and mentorship opportunities constitute important supports for improving diversity and equity in midwifery. They also expressed a need for midwives and midwifery organizations to actively work to disrupt racism and the power structures in midwifery that enable racial inequity to proliferate. DISCUSSION: The manifestations of racism in midwifery have negative impacts on the career trajectory, career satisfaction, interpersonal relationships, and well-being of BIPOC midwives. It is crucial to understand the role of racism in midwifery and make meaningful changes toward dismantling interpersonal and systemic racism in the profession. These progressive changes will serve to create a more diverse and equitable profession, where all midwives can belong and thrive.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".