Mapping Intersectionality in HIV Care and Prevention: A Scoping Review of Engagement, Disengagement, and the Social and Structural Determinants of Health
Bibliographic record
Abstract
Sustained engagement in HIV care is critical for achieving viral suppression, reducing transmission risk, and improving overall health and well-being among people living with HIV (World Health Organization, 2021). Yet, despite decades of global progress in HIV care and prevention, access to and engagement across the HIV care continuum remain uneven. HIV care and prevention gaps disproportionately affect equity-deserving populations including people of colour, transgender and gender-diverse individuals, and those facing socioeconomic disadvantage (Milloy et al., 2012; Poteat et al., 2015), resulting in increased morbidity, mortality, and persistent health inequities (Geng et al., 2010). A growing body of evidence highlights the role of social and structural determinants of health such as poverty, homelessness, criminalization, racism, and colonialism in shaping HIV risk and participation in care (Bukowski et al., 2018; Ontario HIV Treatment Network, 2025). These determinants generate not only logistical barriers such as housing instability and limited transportation (Aidala et al., 2016; Odediran et al., 2022) but also layered systemic obstacles such as medical mistrust, exclusionary policies, and intersectional stigma (Burke et al., 2024; Hall et al., 2017; Logie et al., 2011; Odhiambo et al., 2023; Turan et al., 2017), all of which constrain individuals’ ability to access and sustain care. In response to these compounding inequities, intersectionality has emerged as a critical lens for understanding HIV disparities. Rooted in Black feminist theory, intersectionality is a theoretical framework that illuminates the lived experiences of equity-deserving populations within the systems of oppression (Crenshaw, 1989, 1991). Intersectionality posits that these groups experience unequal, unfair treatment within the systems of oppression (e.g., racism, colonialism), which ultimately leads to health inequity (Collins, 1993). Consequently, intersectionality underscores the importance of addressing the social and structural factors that shape and exacerbate intersectional oppression and exclusion (Collins, 2000; Grzanka, 2018; Moradi, 2016; Moradi & Grzanka, 2017). While there is growing interest in applying intersectionality within HIV research, there remains limited clarity on how it is adopted and operationalized within HIV care engagement and prevention research. Furthermore, there is a lack of conceptual consistency in how engagement and disengagement are defined particularly for individuals who drop out of care early or never engaged with care plans at all (Mayer et al., 2013). Much of existing literature focuses on individuals who remain in care or who re-engage after temporary lapses, thereby reinforcing survivorship bias and limiting our understanding of those most excluded by the system.
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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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".