An Internet-Based Intervention to Increase the Ability of Lesbian, Gay, and Bisexual People to Cope With Adverse Events: Single-Group Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Lesbian, gay, bisexual, transgender, and queer (LGBTQ+) people are at higher risk of mental health problems due to widespread hetero- and cisnormativity, including negative public attitudes toward the LGBTQ+ community. In addition to combating social exclusion at the societal level, strengthening the coping abilities of young LGBTQ+ people is an important goal. OBJECTIVE: In this transdiagnostic feasibility study, we tested a 6-week internet intervention program designed to increase the ability of nonclinical LGBTQ+ participants to cope with adverse events in their daily lives. The program was based on acceptance and commitment therapy principles. METHODS: The program consists of 6 web-based modules and low-intensity assistance for homework provided by a single care provider asynchronously. The design was a single-group assignment of 15 self-identified LGB community members who agreed to participate in an open trial with a single group (pre- and postintervention design). RESULTS: Before starting the program, participants found the intervention credible and expressed high satisfaction at the end of the intervention. Treatment adherence, operationalized by the percentage of completed homework assignments (32/36, 88%) was also high. When we compared participants' pre- and postintervention scores, we found a significant decrease in clinical symptoms of depression (Cohen d=0.44, 90% CI 0.09-0.80), social phobia (d=0.39, 90% CI 0.07-0.72), and posttraumatic stress disorder (d=0.30, 90% CI 0.04-0.55). There was also a significant improvement in the level of self-acceptance and behavioral effectiveness (d=0.64, 90% CI 0.28-0.99) and a significant decrease in the tendency to avoid negative internal experiences (d=0.38, 90% CI 0.09-0.66). The level of general anxiety disorder (P=.11; d=0.29, 90% CI -0.10 to 0.68) and alcohol consumption (P=.35; d=-0.06, 90% CI -0.31 to 0.19) were the only 2 outcomes for which the results were not statistically significant. CONCLUSIONS: The proposed web-based acceptance and commitment therapy program, designed to help LGBTQ+ participants better manage emotional difficulties and become more resilient, represents a promising therapeutic tool. The program could be further tested with more participants to ensure its efficacy and effectiveness. TRIAL REGISTRATION: ClinicalTrials.gov NCT05514964; https://clinicaltrials.gov/study/NCT05514964.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".