Impact of Experienced HIV Stigma on Health is Mediated by Internalized Stigma and Depression: Results From the People Living With HIV Stigma Index in Ontario
Bibliographic record
Abstract
Abstract Background: Experiences of HIV stigma remain prevalent across Canada, causing significant stress and negatively affecting the health and wellbeing of people living with HIV. While studies have consistently demonstrated that stigma negatively impacts health, there has been limited research on the mechanisms behind these effects. This study aims to identify which dimensions of stigma have significant relationships with self-rated health and examine the mechanisms by which those types of stigma impact self-rated health and by extension, key real-life health outcomes.Methods: We recruited 724 participants to complete the People Living with HIV Stigma Index in Ontario, designed by people living with HIV to measure nuanced changes in stigma and discrimination. The present study utilizes data from externally validated measures of stigma and health risks that were included in the survey. First, we conducted multiple regression analyses to examine which variables had a significant impact on self-rated health. Results from the multiple regression guided the mediation analysis. A parallel mediation model was created with enacted stigma as the antecedent, internalized stigma and depression as the mediators, and self-rated health as the outcome. Results: In the multiple regression analysis, internalized stigma (coefficient = -0.20, p<0.01) and depression (coefficient = -0.07, p<0.01) were both significant and independent predictors of health. We found through mediation analyses that the relationship between enacted stigma and self-rated health is mediated in parallel by both internalized stigma [coefficient = -0.08, se = 0.03, 95% CI (-0.14, -0.02)] and depression [coefficient = -0.16, se = 0.03, 95% CI (-0.22, -0.11)].Conclusions: We developed a mediation model to explain how HIV stigma negatively impacts health. We found that that experiences of prejudice or discrimination (enacted stigma) can lead to internalization of negative thoughts regarding one’s HIV status (internalized stigma) and/or increased depressive symptoms which then may lead to worse overall health. Highlighting the importance of internalized stigma and depression has the potential to shape the development of targeted intervention strategies aimed at reducing the burden of stigma and improving the health and wellbeing of people living with HIV.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".