Being a Black Mother Living with HIV Is a “Whole Story”: Implications for Intersectionality Approach
Bibliographic record
Abstract
While African, Caribbean, and Black (ACB) mothers living with HIV in Canada are required to follow public health guidelines by exclusively formula feeding their infants, they also face cultural expectations from peers and family members to breastfeed. They face multiple challenges because of their race, ethnicity, gender, class, and geographical location, among other factors. Previously published studies on this subject did not analyze how the intersectionality of these factors impacts Black mothers’ infant feeding experiences. In this article, we discuss the infant feeding practices of Black mothers living with HIV in Ottawa (Canada). We followed a qualitative methods research design that utilized intersectionality and a community-based participatory research approach. We used the intersectionality framework as a lens to analyze the complex mesh of determinants influencing motherhood experiences of ACB women living with HIV. Being a Black/ACB mother while living with HIV is a “whole story” permeated with cutting-across issues such as race, class, gender, socio-political, and cultural contexts. These issues are interwoven and often difficult to unravel. Multiple layers of structural determinants of Black/ACB women’s HIV vulnerability and health are described. Intersectionality is important for an in-depth understanding of societal power dynamics and their impact on women’s health inequities.
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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 | 0.037 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.033 | 0.080 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.003 | 0.007 |
| 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".