Intersectional Discrimination in Mental Health Care: A Systematic Review With Qualitative Evidence Synthesis
Bibliographic record
Abstract
OBJECTIVE: Discriminatory practices in mental health care undermine the right to health of marginalized service users. Intersectional approaches enable consideration of multiple forms of discrimination that occur simultaneously and remain invisible in single-axis analyses. The authors reviewed intersectionality-informed qualitative literature on discriminatory practices in mental health care to better understand the experiences of marginalized service users and their evaluation and navigation of mental health care. METHODS: The authors searched EBSCO, PubMed, MEDLINE, and JSTOR for studies published January 1, 1989-December 14, 2022. Qualitative and mixed-methods studies were eligible if they used an intersectional approach to examine discrimination (experiences, mechanisms, and coping strategies) in mental health care settings from the perspective of service users and providers. A qualitative evidence synthesis with thematic analysis was performed. RESULTS: Fifteen studies were included in the qualitative evidence synthesis. These studies represented the experiences of 383 service users and 114 providers. Most studies considered the intersections of mental illness with race, sexual and gender diversity, or both and were performed in the United States or Canada. Four themes were identified: the relevance of social identity in mental health care settings, knowledge-related concerns in mental health care, microaggressions in clinical practice, and service users' responses to discriminatory practices. CONCLUSIONS: Discriminatory practices in mental health care lead to specific barriers to care for multiply marginalized service users. Universities and hospitals may improve care by building competencies in recognizing and preventing discrimination through institutionalized training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".