Healthcare access and barriers to utilization among transgender and gender diverse people in Africa: a systematic review
Bibliographic record
Abstract
Background: Transgender and gender diverse (TGD) people face significant challenges in accessing timely, culturally competent, and adequate healthcare due to structural and systemic barriers, yet there is a lack of research exploring the access and utilization of healthcare services within African TGD communities. To address this gap, this systematic review explored: (1) barriers to accessing healthcare services and gender-affirming hormone therapy (GAHT) faced by TGD people, (2) demographic and societal factors correlated with the utilization of healthcare services and GAHT, (3) common healthcare and support services utilized by TGD people, and (4) patterns of accessing healthcare services and GAHT within TGD communities. Methods: A systematic literature search was conducted in PubMed, Embase, and Scopus in September 2023. Eligible studies included peer-reviewed original research, reports, and summaries published in the English language assessing health service accessibility and utilization of TGD people in Africa between January 2016 and December 2023. Results: From 2072 potentially relevant articles, 159 were assessed for eligibility following duplicate removal, and 49 were included for analysis. Forty-five articles addressed barriers to accessing healthcare services and GAHT, seven focused on demographic and societal factors correlated with the utilization of healthcare services and GAHT, 16 covered common healthcare and support services utilized by TGD people, and seven examined patterns of accessing healthcare services and GAHT. Findings suggested a limited availability of health services, inadequate knowledge of TGD healthcare needs among healthcare providers, a lack of recognition of TGD people in healthcare settings, healthcare-related stigma, and financial constraints within African TGD communities. An absence of studies conducted in Northern and Central Africa was identified. Conclusions: TGD people in Africa encounter significant barriers when seeking healthcare services, leading to disparity in the utilization of healthcare and resulting in a disproportionate burden of health risks. The implications of these barriers highlight the urgent need for more high-quality evidence to promote health equity for African TGD people. Trial registration: PROSPERO CRD42024532405. Supplementary Information: The online version contains supplementary material available at 10.1186/s44263-024-00073-2.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".