Longitudinal associations between social determinants of health and well‐being among women living with <scp>HIV</scp> in Canada: A latent class analysis
Bibliographic record
Abstract
BACKGROUND: Social determinants of health (SDoH) can significantly impact overall well-being. While existing research has explored SDoH as predictors of well-being among women living with HIV, longitudinal studies examining these relationships over time remain limited. We examined SDoH typologies among women living with HIV in Canada and longitudinal associations with well-being. METHODS: Using longitudinal survey data collected at three time points from women living with HIV in Canada (2013-2018), we conducted latent class analysis (LCA) to identify subgroups of SDoH indicators, including income, experiences of violence, food security, substance use, housing stability, HIV-related stigma and social support at baseline (Time-1). Multivariable linear and logistic regression examined associations between SDoH classes and well-being (depression, discrimination [gender, racial] and HIV clinical outcomes [viral load, adherence, HIV care barriers]) at Time-3. RESULTS: We identified three distinct SDoH classes among participants (n = 1422, mean age = 42.8): high (n = 435; 30.6%), medium (n = 377; 26.5%) and low SDoH adversity (n = 610; 42.9%). In multivariate regression analyses, the high SDoH adversity class had lower odds of achieving an undetectable viral load (adjusted Odds Ratio [aOR] = 0.46; 95% CI: 0.21, 1.01; p = 0.050) and higher probability of facing barriers to accessing care (aβ = 0.32; 95% CI: 0.19, 0.45; p < 0.001), depression (aOR = 2.52; 95% CI: 1.71, 3.71; p < 0.001), racial discrimination (aβ = 3.42; 95% CI: 1.72, 5.12; p < 0.001) and gender discrimination (aβ = 3.14; 95% CI: 1.42, 4.87; p < 0.001), compared with the low SDoH adversity class at 5-year follow-up. CONCLUSIONS: SDoH adversities were associated with poor wellbeing among women living with HIV in Canada. Integrated, comprehensive person-centred care approaches that address SDoH are needed to improve health and wellbeing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".