Examining Mechanisms Linking Stigma and Health-Related Quality of Life in People Living with HIV
Bibliographic record
Abstract
HIV stigma continues to be one of the biggest barriers to good health and wellbeing for people living with HIV in Canada. While research has demonstrated the negative impact of stigma on a variety of important health outcomes, there is less understanding around exactly how this happens. This dissertation aims to shed light on this phenomenon by quantitatively modelling potential pathways through which stigma affects health-related quality of life for people living with HIV. This work uses data from the People Living with HIV Stigma Index in Ontario, a survey tool designed by and for people living with HIV that contains externally validated quantitative scales to measure stigma and health factors. Data was collected at baseline (t1) and at follow-up (t2) approximately one to two years later using the same measures. This work is built on a series of studies centered around mediation and moderation analyses that examine how stigma affects health-related quality of life and the factors that may alter this relationship. The first two studies examined how the impact of enacted stigma on health was mediated by internalized stigma and depression (Chapter 4) and anticipated stigma (Chapter 5). The next two studies examined the moderating effect of various determinants of health (Chapter 6) and social support (Chapter 7) on the relationship between stigma and health. These empirical analyses were accompanied by a description of the process of greater and meaningful engagement of people living with HIV that was implemented throughout the work (Chapter 8). This dissertation culminates in the Stigma and Health-Related Quality of Life Framework which describes the path from initial stigma marking to how individuals react through a selection of stigma mechanisms (internalized, enacted, and anticipated stigma) and the role of depression, different types of social support, and various determinants of health (age, sexual orientation, region, basic needs) in mediating or moderating the relationship between stigma and health. Outlining these pathways through which stigma acts to affect health can help in identifying targets for stigma reduction interventions that will be efficient and effective at 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".