Generating Intervention Concepts for Reducing Adolescent Relationship Abuse Inequities Among Sexual and Gender Minority Youth: Protocol for a Web-Based, Longitudinal, Human-Centered Design Study
Bibliographic record
Abstract
BACKGROUND: Sexual and gender minority youth (SGMY; eg, lesbian, gay, bisexual, and transgender youth) are at greater risk than their cisgender heterosexual peers for adolescent relationship abuse (ARA; physical, sexual, or psychological abuse in a romantic relationship). However, there is a dearth of efficacious interventions for reducing ARA among SGMY. To address this intervention gap, we designed a novel web-based methodology leveraging the field of human-centered design to generate multiple ARA intervention concepts with SGMY. OBJECTIVE: This paper aims to describe study procedures for a pilot study to rigorously test the feasibility, acceptability, and appropriateness of using web-based human-centered design methods with SGMY to create novel, stakeholder-driven ARA intervention concepts. METHODS: We are conducting a longitudinal, web-based human-centered design study with 45-60 SGMY (aged between 14 and 18 years) recruited via social media from across the United States. Using MURAL (a collaborative, visual web-based workspace) and Zoom (a videoconferencing platform), the SGMY will participate in four group-based sessions (1.5 hours each). In session 1, the SGMY will use rose-thorn-bud to individually document their ideas about healthy and unhealthy relationship characteristics and then use affinity clustering as a group to categorize their self-reported ideas based on similarities and differences. In session 2, the SGMY will use rose-thorn-bud to individually critique a universal evidence-based intervention to reduce ARA and affinity clustering to aggregate their ideas as a group. In session 3, the SGMY will use a creative matrix to generate intervention ideas for reducing ARA among them and force-rank the intervention ideas based on their potential ease of implementation and potential impact using an importance-difficulty matrix. In session 4, the SGMY will generate and refine intervention concepts (from session 3 ideations) to reduce ARA using round robin (for rapid iteration) and concept poster (for fleshing out ideas more fully). We will use content analyses to document the intervention concepts. In a follow-up survey, the SGMY will complete validated measures about the feasibility, acceptability, and appropriateness of the web-based human-centered design methods (a priori benchmarks for success: means >3.75 on each 5-point scale). RESULTS: This study was funded in February 2020. Data collection began in August 2020 and will be completed by April 2021. CONCLUSIONS: Through rigorous testing of the feasibility of our web-based human-centered design methodology, our study may help demonstrate the use of human-centered design methods to engage harder-to-reach stakeholders and actively involve them in the co-creation of relevant interventions. Successful completion of this project also has the potential to catalyze intervention research to address ARA inequities for SGMY. Finally, our approach may be transferable to other populations and health topics, thereby advancing prevention science and health equity. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/26554.
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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 | 0.045 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".