A Mixed-Method Study Protocol of a Novel Psychological Intervention: Virtual Reality Therapy for LGBT (LGBT-VRT)
Bibliographic record
Abstract
Lesbian, gay, bisexual, and transgender (LGBT) people encounter substantial minority stress, which puts them at higher risk of negative mental health outcomes. Despite higher prevalence of mood problems and therapy usage, traditional psychological services are not necessarily meeting their clinical needs, partly due to social isolation, discrimination, limited connections to LGBT communities, and barriers to adequate health care. To address the clinical gap, virtual reality (VR), which has been appraised as a revolutionary tool in the field of clinical psychology, appears to have unlimited potential addressing the unmet needs. A novel psychological intervention which is referred as Virtual Reality Therapy for LGBT (LGBT-VRT) will be developed in the current study. LGBT-VRT is a psychological intervention with theoretical foundations based on stress coping theory, learning theory, self-determination theory, and health equity promotion model. It intends to relieve emotional distress, facilitate social support, build resilience, and ultimately promote quality-of-life of the LGBT people. Using a mixed-method design, the current study aims to test the feasibility and usability of LGBT-VRT as a new protocol, determine effect sizes and identify suggestions for improvement when designing a subsequent larger clinical trial. To our knowledge, the current study is the first to explore the use of VR among LGBT population. This manuscript makes important contributions to the literature examining the clinical gap among the LGBT population, the potential of VR to address their unmet needs; and an innovative solution to the growing need of mental health among LGBT population. The insights of our studies should inform both therapists and public policy makers, and ultimately the LGBT population.
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".