Intersectional stigma and its impact on HIV prevention and care among MSM and WSW in sub-Saharan African countries: a protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Research has established that various forms of stigma (HIV stigma, gender non-conforming stigma and same-gender sex stigma) exist across Sub-Saharan Africa and have consequences for the utilisation of HIV prevention and care services. Stigmas are typically investigated in HIV literature individually or through investigating individual populations and the various stigmas they may face. The concept of intersectionality highlights the interconnected nature of social categorisations and their ability to create interdependent systems of discrimination based on gender, race, sexuality and so on. Drawing from perspectives on intersectionality, intersectional stigma denotes the convergence of multiple marginalised identities within an individual or a group, the experiences of stigma associated with these identities as well as the synergistic impact of these experiences on health and well-being. With respect to HIV, public health scholars can examine the impacts of intersectional stigmas on HIV prevention and care utilisation. METHODS AND ANALYSIS: Reviewers will search systematically through MEDLINE, Global Health, Embase, Scopus, Web of Science Core Collection and Africa Index Medicus and citations for quantitative studies, qualitative studies and grey literature that include data on stigma and HIV among men who have sex with men and women who have sex with women in Sub-Saharan Africa. Eligible studies will include primary or secondary data on stigma related to HIV risk factors experienced by this population. Studies will be written in French or English and be published between January 1991 and November 2020. All screening and data extraction will be performed in duplicate, and if discrepancies arise, they will be settled by GM'RA, LEN, DD or AO. Findings from this study will be reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. ETHICS AND DISSEMINATION: Ethics approval is not required as there will be no human participants and no protected data will be used in this study. We will disseminate findings through peer-reviewed manuscripts, conferences and webinars.
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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 | 0.102 | 0.106 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.030 | 0.021 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".