Anti-Black Medical Gaslighting in Healthcare: Experiences of Black Women in Canada
Bibliographic record
Abstract
BackgroundStereotype about Black people contribute to nurses and healthcare providers gaslighting and dismissing of their health concerns. Despite the popularity of the term medical gaslighting in mainstream literature, few studies have explored the experiences of Black women during pregnancy and childbirth.PurposeThis paper aims to provide an in-depth insight into Black women's experiences of anti-Black medical gaslighting when accessing care during pregnancy and childbirth.MethodsUtilizing qualitative methods, we conducted 24 semi-structured interviews with Black women in the Greater Toronto Area. We used thematic analysis to ground the data analysis and to generate insight into Black women's experiences.ResultsThree overarching themes: 1) Not Being Understood: Privileging of Medical Knowledge Contributing to the Downplaying of Health Concerns, 2) Not Being Believed: Stereotypes Contributing to Dismissive Healthcare Encounters and 3) Listen to Us: Turning off the Cycle of Medical Gaslighting. These themes highlight ways anti-Black medical gaslighting manifests in Black women's healthcare encounters to create differential access to treatment and care.ConclusionsAnti-Black medical gaslighting contributes to differential access to treatment and care. Improving equitable access to treatment and care must involve addressing structural and epistemic biases in healthcare and fostering a culture of listening to humanize the experience of illness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.039 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".