Associations Between HIV-Related Stigma, Racial Discrimination, Gender Discrimination, and Depression Among HIV-Positive African, Caribbean, and Black Women in Ontario, Canada
Bibliographic record
Abstract
Abstract African, Caribbean, and Black (ACB) women are greatly overrepresented in new HIV infections in comparison with Canada's general population. Social and structural factors such as HIV-related stigma, gender discrimination, and racial discrimination converge to increase vulnerability to HIV infection among ACB women by reducing access to HIV prevention services. Stigma and discrimination also present barriers to treatment, care, and support and may contribute to mental health problems. We administered a cross-sectional survey to HIV-positive ACB women (n=173) across Ontario in order to examine the relationships between HIV-related stigma, gender discrimination, racial discrimination, and depression. One-third of participants reported moderate/severe depression scores using the Beck Depression Inventory Fast-Screen guidelines. Hierarchical block regression, moderation, and mediation analyses were conducted to measure associations between independent (HIV-related stigma, gender discrimination, racial discrimination), moderator/mediator (social support, resilient coping), and dependent (depression) variables. Findings included: (1) HIV-related stigma was associated with increased depression; (2) resilient coping was associated with reduced depression but did not moderate the influence of HIV-related stigma on depression; and (3) the effects of HIV-related stigma on depression were partially mediated through resilient coping. HIV-related stigma, gender discrimination, and racial discrimination were significantly correlated with one another and with depression, highlighting the salience of examining multiple intersecting forms of stigma. Generalizability of findings may be limited due to nonrandom sampling. Findings emphasize the importance of multi-component interventions, including building resilient coping skills, mental health promotion and assessment, and stigma reduction programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".