Suicidal Thoughts and Behaviors Among Transgender Adults in Relation to Education, Ethnicity, and Income: A Systematic Review
Bibliographic record
Abstract
Abstract Introduction: This systematic review assessed the impact of race/ethnicity, education, and income on transgender individual's lifetime experience of suicidal thoughts and behaviors (SITB) in gray and published literature (1997–2017). Methods: Sixty four research projects (108 articles) were identified in WorldCat, PubMed, and Google Scholar. Articles were included if they were published in Canada or the United States, included original quantifiable data on transgender SITBs, and had ≥5 participants, at least 51% of whom were ≥18 years. Results: Across all projects suicide ideation averaged 46.55% and attempts averaged 27.19%. The majority of participants were Caucasian, whereas the highest rate of suicide attempts (55.31%) was among First Nations, who accounted for <1.5% of participants. Caucasians, by contrast, had the lowest attempt rate (36.80%). More participants obtained a bachelor's degree and fewer an associate or technical degree than any other level of education. Suicide attempts were highest among those with ≤some high school (50.70%) and lowest among those with an advanced degree (30.25%). More participants made an income of $20–$50,000/year and less $10–$20,000 than any other income bracket. Conclusion: SITBs, among the transgender population, are both universally high and impacted by race/ethnicity, educational attainment, and income. These findings may be useful in creating culturally and factually informed interventions for transgender individuals experiencing SITBs and in informing future research on this topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".