Mapping the missing: a scoping review identifying critically underrepresented LGBTQI+ youth within online sexual, reproductive, and transgender healthcare research
Bibliographic record
Abstract
Online sexual, reproductive, and transgender healthcare can overcome barriers to care among lesbian, gay, bisexual, trans, queer/questioning, intersex, and other (LGBTQI+) youth and address disproportionately poor sexual and reproductive health outcomes. However, LGBTQI + youth are heterogenous and online healthcare spans broad health topics and online platforms. To map recent research and identify gaps, we conducted a scoping review, following Joanna Briggs Institute methodology, using the Participants (LGBTQI + youth aged 10-35 years), Concept (online sexual, reproductive, and transgender healthcare), Context (high-income countries) eligibility framework. We searched nine databases for recent literature (2018-2024), two reviewers screened studies using Rayyan, and data were extracted to Excel and analysed descriptively (N = 132 included papers). Most papers (89/132) were from distinct studies; 43/132 were from 15 studies. There were quantitative (57/132), qualitative (41/132), and mixed methods studies (34/132). Most focused on sexual healthcare (95/132) including HIV/STI prevention (68/95) and HIV management (10/95); 30/132 on transgender healthcare; and only 3/132 on reproductive healthcare. Most targeted young men who have sex with men (79/132) or trans and gender-diverse youth (44/132). Only 4/132 targeted young sexual minority women. Almost all were from the US (119/132). Amid a global shift to delivering healthcare online, this timely review provides the first comprehensive map of critical blind spots, highlighting the urgency of research on reproductive health, sexual wellbeing, and sexual minority women. Addressing these gaps is essential for providing equitable healthcare and reducing health disparities. These findings can guide the delivery of online healthcare that meets the needs of all LGBTQI + youth.
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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 | 0.091 | 0.313 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.064 | 0.048 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".