A Structural Equation Model of Factors Contributing to Quality of Life Among African and Caribbean Women Living with HIV in Ontario, Canada
Bibliographic record
Abstract
African and Caribbean Black (ACB) women in Canada are disproportionately impacted by new HIV infections. ACB women's HIV vulnerability is shaped by contexts of stigma and discrimination. HIV-related stigma compromises quality of life (QOL) among women living with HIV (WLWH), yet scant research has examined concomitant effects of racial discrimination and HIV-related stigma on QOL. We used data from a cross-sectional survey with ACB WLWH in Ontario (n = 173) to test a conceptual model of pathways between HIV-related stigma, racial discrimination, depression, social support, and QOL. We conducted structural equation modeling using maximum likelihood estimation to test the model. In independent models, HIV-related stigma was associated with lower QOL, and depression partially mediated the association between HIV-related stigma and QOL. In the simultaneous model, HIV-related stigma had significant direct effects on depression, social support, and an indirect effect on QOL. When social support was added as a mediator, the direct effect between HIV-related stigma and QOL was no longer significant, suggesting mediation. Racial discrimination had significant direct effects on HIV-related stigma, depression, and social support and an indirect effect on QOL. QOL was associated with higher social support and lower depression scores. The model fit the data well: χ2 = 203.266, degrees of freedom (DF): 112, p < .0001; Comparative Fit Index (CFI): 0.929, Tucker-Lewis Index (TLI): 0.912, Root-Mean Square Error of Approximation (RMSEA): 0.071. We found racial discrimination was associated with increased HIV-related stigma, and HIV-related stigma and racial discrimination compromised QOL. Findings suggest the need for multilevel interventions to reduce stigma and discrimination, address depression, and build social support to improve QOL among ACB WLWH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".