Understanding the Health-related Quality of Life Among Immigrant Women with HIV in Canada
Bibliographic record
Abstract
Social and health disparities experienced by immigrant women with HIV are complex, dynamic and multidimensional. Immigration stress, gender-related stress and HIV-related stress are pervasive and salient among immigrant women with HIV. While research on health-related quality of life (HR-QoL) has expanded for the general population of people with HIV and specific ethnic groups, there is a notable lack of representation and investigation concerning women born outside of Canada in HIV research. Very few studies have delved into the stressors experienced by immigrant women with HIV from socio-ecological or longitudinal perspectives. Therefore, it is imperative to conduct a comprehensive examination of HR-QoL among immigrant women with HIV in Canada. This thesis comprises an introductory section, followed by three distinct standalone research papers, and concludes with a summarizing segment. All three papers in this thesis utilize secondary quantitative survey data from the Canadian HIV Women’s Sexual and Reproductive Health Cohort Study. Paper one uses baseline data and explores the relationships between immigration stress (language barriers, post-traumatic stress disorder (PTSD) symptoms, economic insecurity and racial discrimination) and HR-QoL among immigrant women with HIV, and the mediating effect of social support, including interpersonal and organizational support in this relationship for immigrant women with HIV. Paper two utilizes data from wave 1 and wave 3 and examines the cross-lagged impact of gender-related stress (number of dependents, marital status, recent gender-based violence, and gender discrimination) on HR-QoL and the role of organizational social support among immigrant women with HIV. Paper three used three waves of data and investigates the patterns of change and rates of change in HIV-related stigma and resilience and how they relate to HR-QoL over time. This thesis addresses a notable research gap by focusing on the HR-QoL among immigrant women with HIV in Canada. The implications of this study extend beyond academia. The findings of this study can inform healthcare policies, interventions, and programs aimed at improving the HR-QoL of immigrant women with HIV. By addressing the well-being of immigrant women with HIV, this thesis aligns with broader social goals of inclusivity, diversity, and equitable healthcare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".