LGBT+ inclusion and human rights in Thailand: a scoping review of the literature
Bibliographic record
Abstract
BACKGROUND: Globally, LGBT+ people continue to struggle to achieve full realization of their human rights. Amid reported health and mental health disparities, and economic insecurity, we conducted a scoping review to explore the breadth of the literature, map and summarize the evidence, and identify knowledge gaps on LGBT+ inclusion and human rights in Thailand. METHODS: We conducted a scoping review in accordance with the methodology developed by the Joanna Briggs Institute and PRISMA-ScR guidelines. We systematically searched 16 databases for peer-reviewed literature, and government and nongovernmental organization websites for grey literature, published in English or Thai from January 1, 2000-August 21, 2020. Two reviewers independently screened studies according to pre-set criteria. We abstracted and analyzed data on publication characteristics and focal populations, and synthesized findings in six domains of LGBT+ inclusion: political and civic participation, education, family, personal security and violence, economic well-being, and health. RESULTS: The review captured 3327 results in total, which was scoped to 76 peer-reviewed articles and 39 grey literature sources, the majority published after 2010. Gay men and transgender women were the primary focal populations in the peer-reviewed literature, LGBT+ people as a whole in the grey literature. Health was the predominant domain across publications. Key findings include the absence of generalized antidiscrimination legislation for LGBT+ individuals and lack of recourse for transgender individuals to change their legal gender; multifaceted stigma and discrimination in the educational system; social isolation and exclusion in families; disproportionate prevalence of sexual violence and reluctance to report to police; discrimination and marginalization in employment; and LGBT+ disparities in health and mental health. CONCLUSIONS: Future research and programmatic initiatives on LGBT+ inclusion in Thailand should aim to address: 1) understudied populations-lesbian and bisexual women, transmasculine persons; 2) underrepresented topics, including constraints to LGBT+ advocacy; 3) strategic policy initiatives around anti-discrimination laws and legal recognition of same-sex marriage and families; and 4) the need for consistent collection of disaggregated data on LGBT+ persons in education, family, economic, personal security/violence, and health domains in order to assess indicators of inclusion and progress in advancing human rights for LGBT+ people in Thailand.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".