Strategies to support the mental health and well-being of migrant LGBTQIA+ college and university students: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This review will identify and explore strategies employed by health care professionals, higher education institutions, and organizations to support the mental health and well-being of migrant lesbian, gay, bisexual, transgender, queer, intersex, asexual, and more (LGBTQIA+) college and university students. INTRODUCTION: Migrant LGBTQIA+ students face substantial challenges to their identity formation and social development while studying in their newly settled countries. Little is known about how colleges and universities support LGBTQIA+ students' mental health and well-being from the perspectives of migration status, stigma, and discrimination. Understanding what is known about strategies to support migrant LGBTQIA+ students can inform future policy directions for promoting LGBTQIA+ mental health and well-being in the context of higher education. INCLUSION CRITERIA: This review will incorporate literature from all geographical contexts that focuses on strategies to support the mental health and well-being of migrant LGBTQIA+ college and university students. Literature not related to LGBTQIA+ migrant students will be excluded. METHODS: The JBI methodology for scoping reviews will be followed, and the review will be reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. Several databases and sources of gray literature will be searched from 2004 onward. The review will include literature published in any language and will be independently screened by 2 reviewers. A modified JBI data extraction tool will be used and data will be presented as diagrams along with narrative summaries to answer the review questions. REVIEW REGISTRATION: Open Science Framework https://osf.io/bj2hu/.
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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 | 0.088 | 0.084 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".