Mothers Raising Children with Sickle Cell Disease at the Intersection of Race, Gender, and Illness Stigma
Bibliographic record
Abstract
This qualitative study used the long interview method with Canadian mothers of African and Caribbean descent to understand the underresearched experience of raising a child with sickle cell disease (SCD). Mothers' realities were explored through three levels of social organization: daily caregiver coping (micro level); community views of SCD, such as stigma (meso level); and systemic SCD health care provision (macro level). Through the use of population health and structural social work perspectives, mothers' experiences were examined in the context of perceived gender and racial oppression. Saturation was achieved after initial interviews with 10 participants and a four-month postinterview with half of the participants. Mothers commonly reported several daily coping challenges: fear of their children's death, separation anxiety, loss of control over life, helplessness, and loneliness/isolation. SCD stigma interacted with racism, contributed to social isolation, and prevented families from organizing as a group. All mothers perceived racism as a salient factor behind inadequate mainstream SCD health care. Recommendations to improve SCD health care and implications for social work practice and research are discussed. This is the first known Canadian psychosocial study of SCD and investigation into SCD stigma outside of rural Nigeria.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".