Birthing #blackboyjoy: Black Midwives Caring for Black Mothers of Black Boys During Pregnancy and Childbirth
Bibliographic record
Abstract
BACKGROUND: Structural racism mediates all aspects of Black life. The medicalization of pregnancy and childbirth, and its detrimental impacts on Black birth, is well documented. The Black Lives Matter movement has elevated the national consciousness on all aspects of Black life, but significant attention has been directed toward the murder and dehumanization of Black men and boys. Black midwives, caring for Black people, using the Midwives Model of Care© which consistently demonstrates its efficacy and better outcomes for Black people, are uniquely positioned to witness the physical and psychosocial experiences of birthing Black boys in America. METHODS: Between 2011 and 2013, the first author conducted interviews with 22 Black midwives to understand their perceptions of, and experiences in, predominantly white midwifery education programs and professional organizations. Convenience and snowball sampling were used. This paper investigates previously unreported and unexamined data from the original study by focusing on the witness and insight of nine midwives who provided care for Black mothers of boys during pregnancy and childbirth. FINDINGS: The data presented three themes: It's a Boy: On Restlessness and Complicated Uneasiness; Desensitization of Black Death; and, Physiological Impacts of Toxic Stress. CONCLUSIONS: The findings demonstrate that caring for Black people must be simultaneously theorized and executed within an anti-racist, relationship-centered, reproductive justice framework. Black midwives are uniquely positioned to do this work. Greater attention, in practice and in research, is needed to investigate the birth experiences of Black mothers of boys.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".