Socio-demographic, clinical and service use determinants associated with HIV related stigma among people living with HIV/AIDS: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Defining HIV-related stigma (HRS) can be problematic due to structural inequalities, cultural differences, discrimination by health care providers and the limitations of tools measuring stigma for people living with HIV (PLWH). This meta-analysis aimed to determine self-reported HRS and its association with socio-demographic and clinical determinants. METHODS: PubMed, Scopus, Web of Science, PsycInfo, SciELO and Cochrane electronic databases were searched and after reviewing for study duplicates, the full-text of selected articles were assessed for eligibility using Population, Intervention, Comparator, Outcomes criteria. We used fixed and random-effects meta-analysis models to estimate the pooled prevalence, pooled odds ratio (OR) and 95% confidence intervals. RESULTS: Thirty-one studies containing 10,475 participants met the eligibility criteria. Among the potential risk factors: age > 30 years (OR = 0.93, 95%CI = 0.86, 1), living with a spouse (OR = 0.07, 95%CI = 0.02, 0.17), CD4 count < 200 (OR = 0.5, 95% CI = 0.31, 0.68), medication adherence (OR = 0.96, 95%CI = 0.94, 0.99), poor access to care (OR = 0.79, 95%CI = 0.65, 0.93), time since diagnosis, and accessibility to care (OR = 0.37, 95%CI = 0.11, 0.86) were all significantly associated with self-reported HIV stigma among PWLH. CONCLUSION: Stigma is correlated with numerous negative consequences in marginalised populations including PLWH. Considering the negative association that stigma has on HIV prevention and treatment targeted evidence-based stigma reduction interventions are recommended. Interventions that are focused on a particular group, such as healthcare professionals are warranted. Rigorously designed studies with specific and validated outcome measures associated with targeted interventions may help to improve the reduction of HRS for PLWH.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".