‘I Was Shattered and Broken’: Unmasking the Experiences and Responses of Black Canadian to Pregnancy Loss
Bibliographic record
Abstract
BackgroundPregnancy loss remains an invisible tragedy that impacts on the psychosocial well-being of women and their families. Cultural norms and beliefs about pregnancy loss affect how some women respond and process the loss. Yet research about Black Canadian women's experiences of pregnancy loss is lacking. The purpose of this research was to explore Black Canadian women's experiences and responses to pregnancy loss.MethodsA descriptive exploratory qualitative design was used to gain insight into the experiences of Black Canadian women. Semi-structured interviews were conducted with women who identified as Black. Data was analyzed using a thematic analysis approach.ResultsWe purposely recruited and interviewed 32 Black Canadian women who experienced miscarriage, stillbirth, or neonatal death. Three overarching themes were identified: (a) coming to terms with the reality of losing a pregnancy, (b) grappling with the psychosocial burden of losing a pregnancy, and (c) navigating for support after losing a pregnancy.ConclusionAddressing the psychosocial burden of pregnancy loss is critical to promote the well-being of Black Canadian women. Nurses and other healthcare providers must recognize that the impact of pregnancy loss extends beyond the immediate clinical concerns. Therefore, intervention programs and follow up care must take a holistic and culturally responsive approach to address the needs of Black Canadian women beyond the period of the loss.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.047 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".