Black Women's Silent Battle with Eating Disorders: Exploring Eating Disorders Among Black Women and the Barriers to Recognition and Treatment
Bibliographic record
Abstract
An eating disorder is a complex mental illness that manifests through disordered eating habits and behaviors. Awareness concerning disordered eating behavior has increased within the last decade; Eating disorders have often been primarily associated with affluent white women, even though women of all races and socioeconomic backgrounds are affected. Black women, in particular, tend not to appear in mainstream discussions due to cultural narratives and treatment frameworks. This study will examine the barriers that Black women encounter in acknowledging and treating eating disorders including sociocultural implications, and medical bias. In pursuing a mixed-method approach, data collection from structured interviews with 30 Black Canadian women 18-40 years of age was combined with a secondary analysis of existing research literature on eating disorders and racial disparity in mental health care. This analysis takes into consideration the cognitive effect of societal expectations of women’s bodies and institutionalized medical bias, both of which contribute to underdiagnosis and misrepresentation of eating disorders in Black women. Preliminary findings indicate that stereotypes surrounding Black women’s bodies, combined with cultural attitudes toward food and weight, significantly obscure Black women’s experiences with mental health care. Culturally competent interventions that include and advance recognition, prevention, and treatment strategies need to focus on Black women’s experiences with urgency. Closing these disparities will strengthen efforts toward a more equitable and nuanced understanding of the detrimental effects of eating disorders on Black women. In the long term, this study aims to convey the need for increased access to treatment and dialogues around the intersection of health, race, and gender.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".