Sexual health promotion for sexual and gender minorities in primary care: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Sexual and gender minorities (SGMs) face health disparities related to systemic discrimination and barriers to sexual health. Sexual health promotion encompasses strategies that enable individuals, groups and communities to make informed decisions regarding their sexual well-being. Our objective is to describe the existing sexual health promotion interventions tailored for SGMs within the primary care context. METHODS AND ANALYSIS: We will conduct a scoping review and search for articles in 12 medical and social science academic databases on interventions that are targeted towards SGMs in the primary care context in industrialised countries. Searches were conducted on 7 July 2020 and 31 May 2022. We defined sexual health interventions in the inclusion framework as: (1) promote positive sexual health, or sex and relationship education; (2) reduce the incidence of sexually transmitted infections; (3) reduce unintended pregnancies; or (4) change prejudice, stigma and discrimination around sexual health, or increase awareness surrounding positive sex. Two independent reviewers will select articles meeting inclusion criteria and extract data. Participant and study characteristics will be summarised using frequencies and proportions. Our primary analysis will include a descriptive summary of key interventional themes from content and thematic analysis. Gender-based Analysis Plus will be used to stratify themes based on gender, race, sexuality and other identities. The secondary analysis will include the use of the Sexual and Gender Minority Disparities Research Framework to analyse the interventions from a socioecological perspective. ETHICS AND DISSEMINATION: No ethical approval is required for a scoping review. The protocol was registered on the Open Science Framework Registries (https://doi.org/10.17605/OSF.IO/X5R47). The intended audiences are primary care providers, public health, researchers and community-based organisations. Results will be communicated through peer-reviewed publication, conferences, rounds and other opportunities to reach primary care providers. Community-based engagement will occur through presentations, guest speakers, community forums and research summary handouts.
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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 | 0.130 | 0.093 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.022 |
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".