How LGBT+ Young People Use the Internet in Relation to Their Mental Health and Envisage the Use of e-Therapy: Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Lesbian, gay, bisexual, and transgender (LGBT) youth and other young people diverse in terms of their sexuality and gender (LGBT+) are at an elevated risk of mental health problems such as depression. Factors such as isolation and stigma mean that accessing mental health services can be particularly challenging for LGBT+ young people, and previous studies have highlighted that many prefer to access psychological support on the Web. Research from New Zealand has demonstrated promising effectiveness and acceptability for an LGBT+ focused, serious game-based, computerized cognitive behavioral therapy program, Rainbow Smart, Positive, Active, Realistic, X-factor thoughts (SPARX). However, there has been limited research conducted in the area of electronic therapy (e-therapy) for LGBT+ people. OBJECTIVE: This study aimed to explore how and why LGBT+ young people use the internet to support their mental health. This study also sought to explore LGBT+ young people's and professionals' views about e-therapies, drawing on the example of Rainbow SPARX. METHODS: A total of 3 focus groups and 5 semistructured interviews were conducted with 21 LGBT+ young people (aged 15-22 years) and 6 professionals (4 health and social care practitioners and 2 National Health Service commissioners) in England and Wales. A general inductive approach was used to analyze data. RESULTS: LGBT+ youth participants considered that the use of the internet was ubiquitous, and it was valuable for support and information. However, they also thought that internet use could be problematic, and they highlighted certain internet safety and personal security considerations. They drew on a range of gaming experiences and expectations to inform their feedback about Rainbow SPARX. Their responses focused on the need for this e-therapy program to be updated and refined. LGBT+ young people experienced challenges related to stigma and mistreatment, and they suggested that strategies addressing their common challenges should be included in e-therapy content. Professional study participants also emphasized the need to update and refine Rainbow SPARX. Moreover, professionals highlighted some of the issues associated with e-therapies needing to demonstrate effectiveness and challenges associated with health service commissioning processes. CONCLUSIONS: LGBT+ young people use the internet to obtain support and access information, including information related to their mental health. They are interested in LGBT-specific e-therapies; however, these must be in a contemporary format, engaging, and adequately acknowledge the experiences of LGBT+ young people.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".