The relationship between sexual and gender stigma and difficulty accessing primary and mental healthcare services among LGBTQI+ populations in Thailand: Findings from a national survey
Bibliographic record
Abstract
ABSTRACT Sexual and gender stigma is a known contributor to population health inequities; however, its impact on healthcare access among sexual and gender minorities (SGM) in Thailand is understudied. Therefore, we sought to examine the level of SGM stigma and its impact on self-reported difficulty accessing primary and mental healthcare services among a nationally recruited sample of lesbian, gay, bisexual, transgender, queer, intersex, and other gender and sexually diverse (LGBTQI+) people in Thailand. A previously validated sexual stigma scale was adapted to ascertain perceived and enacted SGM stigma. Between January and March 2018, 1,350 LGBTQI+ participants completed the online survey, and the median age was 27 (Quartile 1, 3: 23, 33) years. In total, 169 (12.5%) and 269 (19.9%) reported difficulty accessing primary and mental healthcare and 365 (27.0%) reported actively concealing their gender expression to access care. In multivariable logistic regression analyses, experiences of enacted stigma were independently associated with difficulty accessing primary healthcare (adjusted odds ratio [AOR] = 1.35; 95% Confidence Interval [CI]: 1.11 – 1.63) and mental healthcare (AOR = 1.26; 95% CI: 1.07 – 1.48), while experiences of perceived stigma were independently associated with difficulty accessing mental healthcare only (AOR = 1.20; 95% CI: 1.07 – 1.34). Our findings call for multi-level interventions to decrease SGM stigma and improve healthcare access among SGM in Thailand.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".