Conceptualizing the Impacts of Racism on Racialized Midwives in Ontario: An Alert to the Profession
Bibliographic record
Abstract
INTRODUCTION: There is a research gap on how racism impacts the mental health of midwives in Ontario. Our aim was to conceptualize the impact of racism on racialized midwives in Ontario. METHODS: Informed by constructivist grounded theory, we analyzed data contributed by racialized midwives in Ontario who participated in focus groups and interviews as part of a larger study about mental health. Participants had practiced midwifery within the past 15 months. RESULTS: Seven participants from 2 focus groups and one individual interview were included. Our conceptualization, Hypervigilance: Being Plugged In, describes cause-and-effect relationships between 3 pairs of external exposures and corresponding internal responses. The 3 paired relationships are: (1) microaggressions and social isolation elicit exhaustion, (2) bias checking and systemic exclusion elicit educator fatigue, and (3) Whiteness, the White gaze, and institutional inaction elicit disenfranchisement. Participants identified 2 recommendations to improve the mental health of racialized midwives: (1) identify and fund racially and ethnically concordant mental health practitioners for mental health support and (2) combat racism within the profession by requiring antiracism training as part of annual membership renewal. DISCUSSION: Our research has generated a novel conceptualization explaining how exposure to racism negatively impacts the mental health of midwives. This is further supported by the literature with the concept of allostatic overload, whereby allostasis is no longer possible. When this occurs in the body, it can lead to illness and disability. This signifies an alert to the profession and systems partners to address the impact of racism on the workforce. This study provides insight into racialized midwives' experiences and presents recommendations to counter the impacts of racism.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.022 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".