Co-designing a social media and anxiety survey: reflections on the importance of centring mental health lived experience expertise
Bibliographic record
Abstract
INTRODUCTION: This study explores a collaborative co-design process undertaken with people with lived experience expertise (PLEE), to develop a survey investigating experiences of social media and anxiety. The research is the first step in a larger project across five countries (Australia, the United Kingdom, India, Canada and Singapore) that will seek to validate whether passive smartphone analytics and codesigned ethical protocols can underpin a scalable culturally inclusive AI chatbot that detects and mitigates anxiety based on smartphone use. METHODS: Through three iterative co-design workshops, conducted in Australia, facilitated by and involving people with lived experience of mental health conditions, insights were gathered on psychological, social, and structural mechanisms by which social-media use influences anxiety. RESULTS: Co-design workshop members strongly challenged the research team within five important themes: (1) reframing risk and safety that involved 'calling out' disempowering and discriminatory language inherent in survey processes and existing validated measures; (2) social media as both harm and Haven that emphasised social media as both a source of anxiety and a lifeline for connection for this population; (3) designing for inclusion, accessibility, and safety to ensure survey usability and psychological safety for future participants; (4) transparency, power, and representation to ensure lived experience involvement meant shared ownership, avoided tokenism, included First Nations leadership; and (5) broadening the lens - cultural, physical, and socio-economic factors involved urging a holistic view of the person and a systems view of anxiety and technology. CONCLUSION: By involving people with mental health lived experience expertise in the design process, this study was able to co-create recommendations to strengthen the project's survey design, ethical framework, and implementation plan. The co-design approach ensured the social media and anxiety survey met the specific needs of the target group and was trauma-informed, promoting trust, engagement and feasibility. Future research will aim to focus on gathering insights from similar lived experience co-design workshops in the United Kingdom, India, Canada and Singapore to refine the AI Chatbot prototype and evaluating its effectiveness in a broader study. This study underscores the crucial role of mental health lived experience expertise in research that seeks to test digital solutions for people who experience anxiety exacerbated by social media use. PATIENT AND PUBLIC CONTRIBUTION: People with lived experience of a mental health condition contributed throughout the design, analysis and write-up of this work as members of a Lived Experience Advisory Panel (LEAP) which met over a series of co-design sessions. The co-design was led by a mental health lived experience researcher who was also a key member of the research team for the larger project. They led the reflexive thematic analysis, and writing and reviewing of the manuscript, in partnership with the co-design group members and the wider research team. Whilst some members of the academic research team identified as having mental health lived experience, they did not undertake their research roles from this perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".