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Enregistrement W4416126259 · doi:10.1111/hex.70677

Co-designing a social media and anxiety survey: reflections on the importance of centring mental health lived experience expertise

2025· article· en· W4416126259 sur OpenAlexaboutno aff
Sharon Lawn, Kerri Gillespie, Aakanksha Sahu, Stephanie J. Tobin, Vasundhara Shulka, Joanne Cockle, Amrita Dasvarma, Aislin Gleeson, John Milham, Robyn Priest, Puneet Sansanwal, Ashley Spradbury, Anna Zhang, Christine Kaine, Selena E. Bartlett

Notice bibliographique

RevueHealth Expectations · 2025
Typearticle
Langueen
DomainePsychology
ThématiqueDigital Mental Health Interventions
Établissements canadiensnon disponible
Organismes subventionnairesFlinders UniversityWellcome Trust
Mots-clésCognitive reframingMental healthSocial mediaLived experienceHarmAnxietyIdentity (music)Qualitative research

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,474
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,161
Tête enseignante GPT0,489
Écart entre enseignants0,328 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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