The Utilization of Syndemic Theory in Transgender Literature: A Structured Literature Review
Bibliographic record
Abstract
Purpose: Trans and gender diverse (TGD) people are one of the world's most marginalized populations. Current evidence indicates that ostracism faced by these communities leads to multiple adverse conditions. The synergistic impact of these conditions -a syndemic-has been well established among other marginalized populations. Despite disproportionate rates of syndemic conditions, there is a dearth of literature exploring their effect among TGD people. This review sought to summarize the current literature regarding syndemic conditions among this population. Methods: A comprehensive search of databases (Medline, Embase, Global Health, PsycInfo, and CINAHL) was conducted; search terms included syndemics, syndemic theory, transgender, sexual minority, and gender minority. References within selected articles were also reviewed. Inclusion criteria for this review included studies that (1) explored the impact of syndemic factors and/or theory among TGD people, (2) were written in English, and (3) published up to December 2020. Results: The search generated 192 articles; 10 met the inclusion criteria. Substance use, mental health, and victimization were the most prominent syndemic conditions assessed. Current evidence is limited to sexual risk behaviors and provides preliminary support for an association between syndemic conditions and sexual risk behaviors. Conclusion: Understanding syndemics is vital to developing contextually suitable interventions or TGD people. Future researchers should work toward longitudinal or high-level modeling approaches of syndemic theory to effectively design interventions to improve health outcomes among TGD people. Furthermore, scholars should study the relationship between syndemic conditions and other outcomes to better understand the impact of syndemics on broader health consequences.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".