The Perinatal Experience of Black Birthing People in Quebec
Bibliographic record
Abstract
Aim/Purpose: Determine if Black birthing people who delivered their babies in Quebec face more complications, death, and overall worse perinatal experiences than their White counterparts. Background: With the recent surge of research on American Black maternal health demonstrating apparent discrepancies between the rates of Maternal morbidity and mortality, Canada’s lack of interest in this potential issue is more salient than ever. Unlike its southern neighbor, Canada, does not have a concise approach to data collection on maternal health with respect to the birthing parents’ ethnicity or race. Methodology: Qualitative research, including a literature review and the interview of a key informant. The literature review is an analysis of the currently available research on Black maternal health and experience in North America, while the interview tackles the issue on a provincial level. In this 30-minute interview, a medical student and doula answers 15 questions pertaining to the current perinatal conditions of Black birthing people in Quebec. Findings: The perinatal experience of Black birthing people in Quebec is influenced by many factors that are often out of the control of these patients. (1) Having access to Black physicians, (2) having a healthy social support system, (3) having access to complimentary medical resources, (4) the lack of empathy demonstrated by healthcare professionals, (5) determinants of health, and (6) overall culturally unsafe practices are all elements of the perinatal experience that can negatively affect Black birthing parents. Impact on Society: This research could act as a steppingstone for further exhaustive research addressing the Black maternal experience in Quebec. In creating this study, we seek to open the door for more conversations not only on an academic level but hopefully on a juristic level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".