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Record W4392175885 · doi:10.2196/54586

A Web-Based Intervention to Support the Mental Well-Being of Sexual and Gender Minority Young People: Mixed Methods Co-Design of Oneself

2024· article· en· W4392175885 on OpenAlexvenueno aff
Katherine Brown, Mathijs Lucassen, Alicia Núñez‐García, Katharine A. Rimes, Louise Wallace, Rajvinder Samra

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
FundersMedical Research Council
KeywordsSexual minorityIntervention (counseling)PsychologySocial psychologySexual orientationPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Sexual and gender minority youth are at greater risk of compromised mental health than their heterosexual and cisgender peers. This is considered to be due to an increased burden of stigma, discrimination, or bullying resulting in a heightened experience of daily stress. Given the increasing digital accessibility and a strong preference for web-based support among sexual and gender minority youth, digital interventions are a key means to provide support to maintain their well-being. OBJECTIVE: This paper aims to explicate the co-design processes and underpinning logic of Oneself, a bespoke web-based intervention for sexual and gender minority youth. METHODS: This study followed a 6-stage process set out by Hagen et al (identify, define, position, concept, create, and use), incorporating a systematic scoping review of existing evidence, focus groups with 4 stakeholder groups (ie, sexual and gender minority youth, professionals who directly support them, parents, and UK public health service commissioners), a series of co-design workshops and web-based consultations with sexual and gender minority youth, the appointment of a digital development company, and young adult sexual and gender minority contributors to create content grounded in authentic experiences. RESULTS: Oneself features a welcome and home page, including a free accessible to all animation explaining the importance of using appropriate pronouns and the opportunity to create a user account and log-in to access further free content. Creating an account provides an opportunity (for the user and the research team) to record engagement, assess users' well-being, and track progress through the available content. There are three sections of content in Oneself focused on the priority topics identified through co-design: (1) coming out and doing so safely; (2) managing school, including homophobic, biphobic, or transphobic bullying or similar; and (3) dealing with parents and families, especially unsupportive family members, including parents or caregivers. Oneself's content focuses on identifying these as topic areas and providing potential resources to assist sexual and gender minority youth in coping with these areas. For instance, Oneself drew on therapeutic concepts such as cognitive reframing, stress reduction, and problem-solving techniques. There is also a section containing relaxation exercises, a section with links to other recommended support and resources, and a downloads section with more detailed techniques and strategies for improving well-being. CONCLUSIONS: This study contributes to research by opening up the black box of intervention development. It shows how Oneself is underpinned by a logic that can support future development and evaluation and includes diverse co-designers. More interactive techniques to support well-being would be beneficial for further development. Additional content specific to a wider range of intersecting identities (such as care-experienced Asian sexual and gender minority youth from a minority faith background) would also be beneficial in future Oneself developments. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/31036.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.131
GPT teacher head0.544
Teacher spread0.413 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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